A method of operation of power-heat generating units.

专利摘要:
The invention relates to a method (900) for operating a sensor in a heat-generating unit. The method comprises the steps of: defining look-back periods, wherein the look-back periods each comprise previous periods of operation of the heat-generating unit, the look-back periods comprising at least a first lookback period and a second lookback period, receiving a first set of data related to the readings for the sensor during the first look-back period, receiving a second set of data related to the readings for the sensor during the second look-back period, performing a first check on the first set and obtaining a first result (904), performing a second check on the second set and obtaining it a second result (907), and determining a probability of whether the sensor malfunctions based on the first and second results.
公开号:CH710432A2
申请号:CH01700/15
申请日:2015-11-20
公开日:2016-05-31
发明作者:Anne Wichmann Lisa；Kumar Pandey Achalesh；Michael Raczynski Christopher
申请人:Gen Electric；
IPC主号:

专利说明:

CROSS-REFERENCE TO RELATED APPLICATIONS
This application claims priority from the provisional U.S. Pat. Patent Application No. 61/922 555 entitled "TURBINE ENGINE AND PLANT OPERATIONAL FLEXIBILITY AND ECONOMIC OPTIMIZATION SYSTEMS AND PROCESSES RELATED THERETO", filed on Dec. 31, 2013, which is hereby incorporated by reference in its entirety; The present application claims the filing date of this provisional patent application according to 35 U.S.C.119 (e).
BACKGROUND OF THE INVENTION
The invention of the present application relates generally to power generation and, more particularly, to methods and systems associated with economics and performance optimization and / or improvement of power plants having heat generation facilities.
In electric power plants generates a number of participants or power plants electricity, which is then distributed on the basis of common transmission lines to residential and commercial customers. It is understood that heat generation systems, such as gas turbines, steam turbines, and combined cycle systems, will still be used to generate a significant proportion of the power required by such systems. Each of the power plants within such systems has one or more power generation units, and each of these units typically includes a control system that controls operation and, if the power plant has more than one power unit, the power of the power plant as a whole. As an example, one of the responsibilities of a power plant operator is generating a supply curve that represents the cost of power generation. A supply curve typically includes an incremental variable cost curve, an average variable cost curve, or other suitable indication of variable power generation cost, which is typically expressed in dollars per megawatt hour versus output in megawatts. It is understood that an average variable cost curve may represent cumulative cost divided by cumulative power output for a given point, and an incremental variable cost curve may represent a change in cost divided by a change in power output. For example, an incremental variable cost curve is obtained by taking a first derivative of the input-output curve of the power plant that represents cost per hour compared to generated power. In a combined cycle power plant, where waste heat from a fuel burning generator is used to generate steam to power an additional steam turbine, an incremental variable cost curve can also be achieved with known techniques, but their derivative can be more complex ,
Most power systems use a competitive process, commonly called economic dispatch, to split up system load on power plants during a future period of time. As part of this process, power plants produce supply curves at regular intervals and send the supply curves to a power system authority or to a dispatcher. Such supply curves represent bids of the power plants to generate a portion of the electricity that the power system requires during a future market period. The dispatching authority receives the supply curves from the power plants within their system and assesses them to determine the level at which each power plant should be involved to most efficiently meet the system's predicted load requirements. In doing so, the allocation authority analyzes supply curves and, with the aim of determining the lowest generation costs for the system, generates commitment planning that describes the extent to which each of the power plants will be involved during the relevant period.
Once the commitment planning has been communicated to the power plants, each power plant can determine the most efficient and cost-effective way to meet its load obligation. It is understood that the generating units of the power plant have control systems that monitor and control the operation. When the generating units have heat generators, such control systems direct the combustion systems as well as other aspects of the operation. (For illustrative purposes, both a gas turbine and a combined cycle power plant will be described herein, but it will be understood that certain embodiments of the present invention may be applied to or used in conjunction with other types of power generation units.) The control system may execute scheduling algorithms , which adjust the fuel flow, inlet guide vanes and other control inputs to ensure efficient operation of the machine. However, the actual output and efficiency of a power plant is affected by external factors, such as variable environmental conditions, which can not be fully anticipated. It is understood that the complexity of such systems, as well as the variability of operating conditions, can make it difficult to predict and control performance, often resulting in inefficient operation.
Machine degradation that occurs over time is another fact that is difficult to quantify, which can significantly affect the performance of the generating units. It will be understood that the degradation rate, replacement of worn components, timing of maintenance operations, and other factors will affect the short-term performance of the power plant and therefore must be taken into account when generating cost curves during the allocation process, and even if the long-term cost effectiveness of the Power plant is assessed. As an example, gas turbine life typically includes limits expressed in both operating hours and number of starts. If a gas turbine or one of its components reaches its limit of start-up before the operating hours limit, it must be repaired or replaced, even if it has residual hours-based life. Hourly lifetime on a gas turbine can be increased by reducing the firing temperature, but this reduces the efficiency of the gas turbine, which in turn increases operating costs. Conversely, increasing the firing temperature increases efficiency, but shortens gas turbine life and increases maintenance and / or replacement costs. It is understood that the life cycle costs of a heat engine depend on many complex factors, while also representing a significant consideration in terms of the economic efficiency of the power plant.
[0007] Given the complexity of modern power plants, especially those having multiple generating units (power plant blocks) and the market in which they compete, power plant operators have continued to seek to maximize economic viability. For example, grid compatibility and allocation planning for a power plant are adversely affected by controlling heat generating units in an excessively static manner, that is, using static control profiles, such as heating rate curves, derived from only periodic performance tests. Between these periodic updates, turbine performance may change (for example due to deterioration), which may affect startup and load performance. In addition, intraday changes in external factors may result in inefficient operation without their consideration in the turbine control profiles. To compensate for this type of variability, power plant operators often become overly conservative in planning future operations, resulting in underutilized power units. At other times, power plant operators are forced to operate units inefficiently to meet excessive obligations.
Without identifying the short-term inefficiencies and / or long term degradation as they arise each, the conventional control systems of power plants must either be retuned frequently, which is a costly operation, or operated conservatively to preemptively consider component degradation. The alternative means risking violating operating limits, resulting in excessive fatigue or failure. Similarly, conventional power plant control systems lack the ability to account for changing conditions very cost effectively. It goes without saying that this results in the fact that power plant utilization is often not optimal. There is therefore a need for improved methods and systems for monitoring, modeling, and controlling the operation of the power plant, particularly those that provide a complete understanding of the myriad of operating modes available to operators of complex modern power plants and the economic tradeoffs that exist connected with them.
BRIEF DESCRIPTION OF THE INVENTION
The present application therefore describes a method for operating a sensor in a heat generating unit. The sensor may be communicatively connected to a control system and configured to detect readings to measure an operating parameter. The method may include the steps of: defining look-back periods, wherein the look-back periods are each preceding periods of operation of the heat-generating unit, wherein the lookback periods comprise at least a first lookback period and a second lookback period, receiving a first record relating to the readings for the sensor during the first one Review period, receiving a second record relating to the readings for the sensor during the second lookback period, performing a first test on the first data set and obtaining a first result, performing a second test on the second data set and obtaining a second result, and determining one Probability of sensor malfunction based on first and second results.
These and other features of the present application will become apparent upon review of the following detailed description of the preferred embodiments, taken in conjunction with the drawings and the appended claims.
BRIEF DESCRIPTION OF THE DRAWINGS
[0011]<Tb> FIG. 1 <SEP> shows a sketch of a power system according to aspects of the present invention,<Tb> FIG. 2 <SEP> illustrates a sketch of an exemplary heat generation unit as may be used within power plants according to embodiments of the present invention,<Tb> FIG. 3 <SEP> is a sketch of an exemplary power plant having multiple gas turbines in accordance with embodiments of the present invention;<Tb> FIG. 4 <SEP> illustrates an exemplary system configuration of a power plant controller and optimizer in accordance with aspects of the present invention.<Tb> FIG. 5 <SEP> illustrates a schematic of a power plant having a power plant controller and an optimizer having a system configuration according to certain aspects of the present invention,<Tb> FIG. FIG. 6 shows a computer system having an exemplary user interface in accordance with certain aspects of the present invention; FIG.<Tb> FIG. 7 <SEP> is an exemplary incremental heating rate curve and an effect that an error may have on the economic allocation process,<Tb> FIG. 8 <SEP> is a sketch of an exemplary power plant control device having a power system in accordance with aspects of the present invention;<Tb> FIG. 9 <SEP> illustrates a flowchart of the factory control method according to aspects of the present invention,<Tb> FIG. 10 <SEP> illustrates a data flow diagram describing an architecture for a plant optimization system for a combined cycle power plant in accordance with aspects of the present invention;<Tb> FIG. Figure 11 provides a simplified block diagram of a computer system as may be used with a real-time optimization system in accordance with aspects of the present invention.<Tb> FIG. 12 <SEP> is a flowchart of an exemplary method for solving parameterized concurrent equations and constraints according to the present invention;<Tb> FIG. FIG. 13 shows a simplified configuration of a computer system according to control methodology of embodiments of the present invention; FIG.<Tb> FIG. 14 <SEP> illustrates an alternative configuration of a computer system according to control methodology of embodiments of the present invention,<Tb> FIG. 15 <SEP> is a flowchart of an example control methodology in accordance with exemplary aspects of the present invention.<Tb> FIG. 16 <SEP> is a flowchart of an example control methodology according to exemplary aspects of the present invention,<Tb> FIG. 17 <SEP> is a flowchart of an example control methodology according to exemplary aspects of the present invention.<Tb> FIG. FIG. 18 illustrates a flowchart that provides an alternative embodiment of the present invention related to the optimization of a settling process; FIG.<Tb> FIG. 19 <SEP> illustrates a flowchart that provides an alternative embodiment of the present invention that relates to the optimization between trim and shutdown,<Tb> FIG. 20 <SEP> is a sketch illustrating available operating modes of a gas turbine during a selected operating period having defined intervals in accordance with aspects of an exemplary embodiment of the present invention;<Tb> FIG. 21 <SEP> is a sketch illustrating available operating modes of a gas turbine during a selected operating time period having defined intervals in accordance with aspects of an alternative embodiment of the present invention;<Tb> FIG. 22 <SEP> illustrates a flowchart according to a power plant fleet optimization process according to an alternative embodiment of the present invention,<Tb> FIG. 23 <SEP> illustrates a sketch of a power plant fleet optimization system in accordance with aspects of the present invention,<Tb> FIG. 24 <SEP> illustrates a sketch of a power plant fleet optimization system according to alternative aspects of the present invention,<Tb> FIG. 25 <SEP> illustrates a sketch of a power plant fleet optimization system according to alternative aspects of the present invention,<Tb> FIG. 26 <SEP> illustrates a sketch of a method for controlling the operation of power plant sensors according to alternative aspects of the present invention,<Tb> FIG. FIG. 27 <SEP> illustrates an exemplary embodiment of the continuity sensor test of FIG. 26; FIG.<Tb> FIG. 28 <SEP> illustrates an exemplary embodiment of the data sensor test of FIG. 26,<Tb> FIG. FIG. 29 <SEP> illustrates an exemplary embodiment of the model sensor test of FIG. 26. FIG.<Tb> FIG. 30 <SEP> illustrates an exemplary embodiment of the range sensor test of FIGS. 26, and<Tb> FIG. 31 <SEP> illustrates an exemplary embodiment of the averaging sensor test of FIG. 26.
DETAILED DESCRIPTION OF THE INVENTION
Exemplary embodiments of the present invention will now be described in more detail with reference to the accompanying drawings, in which some but not all embodiments of the invention are shown. Namely, different embodiments of the invention may be embodied in many different forms and should not be construed as limited to the embodiments set forth herein, as these embodiments are provided rather for this disclosure to meet applicable legal requirements. Like reference numerals may refer to like elements throughout.
In accordance with aspects of the present invention, systems and methods are disclosed that can be used to optimize the performance of power systems, power plants, and / or heat generation units. In exemplary embodiments, this optimization includes economic optimization whereby a power plant operator decides between alternative modes of operation to increase profitability. Embodiments may be used within a particular power system to provide a competitive advantage in obtaining favorable business engagement conditions during the allocation process. A consultant function may allow operators to choose between operating modes based on precise economic comparisons and forecasts. As another feature, the proactive fuel purchase process may be improved over future generation periods so that fuel inventory is minimized while not increasing the risk of underfill. Other configurations of the present invention, as described below, provide computer implemented methods and apparatus for modeling power systems and power plants having multiple heat generating units. The technical effects of some configurations of the present invention include the generation and release of power system models that predict performance under varying physical, operational, and / or economic conditions. Exemplary embodiments of the present invention combine a power plant model that predicts performance under varying environmental and operating conditions with an economic model that has economic constraints, objectives, and market conditions to optimize profitability. In doing so, the optimization system of the present invention can predict optimized setpoint targets that maximize profitability for certain combinations of environmental, operating, contract, regulatory, legal and / or economic and market conditions.
FIG. 1 illustrates a schematic representation of a power system 10 having aspects of the present invention, as well as an example environment in which embodiments may operate. Power system 10 may include power generators or power plants 12, such as the illustrated wind and thermal power plants. It is understood that thermal power plants may include generating units, such as gas turbines, coal-fired steam turbines and / or combined cycle plants. Additionally, the power system 10 may include other types of power plants (not shown), such as solar power plants, hydroelectric power plants, geothermal power plants, nuclear power plants, and / or any other suitable power sources now known or later discovered. Transmission lines 14 may connect different power plants 12 to customers or loads 16 of the power system 10. It is understood that the transmission lines 14 may represent a network or distribution network for the power system and may include multiple sections and / or substations as desired or required. The power generated by the power plants 12 can be supplied via transmission lines 14 to 16 loads, which may have, for example, communities, residential or commercial customers. The power system 10 may also include memory devices 18 connected to transmission lines 14 for storing energy during periods of excessive generation.
The power system 10 also includes control systems or controllers 22, 23, 25 that manage or control the operation of a plurality of the components contained therein. For example, a power plant controller 22 may control the operation of each of the power plants 12. Load control devices 23 may control the operation of the various loads 16 associated with the power system 10. For example, a load control device 23 may manage the type or timing of a customer's power purchase. Allocation authority 24 may manage certain aspects of the operation of power system 10 and may include a power system controller 25 that controls the economic allocation policy by which load commitments are distributed among the participating power plants. The control devices 42, 23, 25, which are represented by rectangular blocks, can be connected via communication lines or connections 21 to a communication network 20, via which data is exchanged. The terminals 21 may be wired or wireless. It is understood that the communication network 20 may be connected to or associated with a larger communication system or network, such as the Internet or a private computer network. In addition, the controllers 22, 23, 25 may receive information, data and instructions and / or may send information, data and instructions to data libraries and resources, which may be generically called "data resources 26", over the communications network 20 or, alternatively, one or more store or house several such databases locally. The data resources 26 may include, but are not limited to, several types of data: market data, operational data, and environmental data. Market data includes information about market conditions, such as energy selling prices, fuel costs, labor costs, regulations, etc. Operational data includes information related to the operating conditions of the power plant or its generating units, such as temperature or pressure measurements within the power plant, air flow rates, fuel flow rates, etc. Environmental data contain information related to environmental conditions in the factory, such as ambient air temperature, humidity and / or pressure. Market, operational and environmental data may each contain historical records, current condition data and / or data related to forecasts. The data resources 26 may include, for example, current and projected meteorological / climate information, current and predicted market conditions, usage and performance history records of the operation of the power plant, and / or measured parameters associated with the operation of other power plants having similar components and / or configurations, as well as have other data as appropriate and / or desired. In operation, the power system controller 25 of the dispatching authority 24 may, for example, receive data from and issue instructions to other control devices 22, 23 within the power system 10. Each plant and the load control devices then control and provide information to the system component they are responsible for and receive instructions from the power system controller 25.
FIG. 2 is a sketch of an exemplary heat generation unit, a gas turbine system 30, that may be used in a power plant according to the present invention. As illustrated, the gas turbine system 30 includes a compressor 32, a combustor 34, and a turbine 36 that is drivingly coupled to the compressor 32 and a component controller 31. The component controller 31 may be connected to the power plant controller 22 coupled to a user input device for Receiving communications from an operator 39 may be connected. It is understood that the component control device 31 and the power plant control device 22 may alternatively be combined in a single controller. An inlet conduit 40 channels ambient air to the compressor 32. As discussed in FIG. 3, injected water and / or other humectant may be channeled to the compressor through the inlet conduit 40. The inlet conduit 40 may include filters, shields, and sound absorbing devices that contribute to a pressure loss of the ambient air flowing through the inlet conduit 40 into the inlet vane wheels 41 of the compressor 32. An exhaust conduit 42 channels combustion gases from an outlet of the turbine 36, for example, through emissions control and sound absorbing devices. The sound absorbing materials and emission control devices may apply back pressure to the turbine 36. The turbine 36 may drive a generator 44 that provides power that may then be distributed by the power system 10 over the transmission lines 14.
The operation of the gas turbine system 30 may be monitored by a plurality of sensors 46 that sense different operating conditions or parameters therein, including, for example, conditions within the compressor 32, the combustor 34, the turbine 36, the generator 44, and the environment 33. Temperature sensors 46 may monitor, for example, ambient temperatures, compressor outlet temperature, turbine exhaust temperature, and other temperatures within the flow path of the gas turbine system 30. Similarly, pressure sensors 46 may monitor ambient pressure, static and dynamic pressure levels at the compressor inlet, compressor outlet, turbine exhaust, and at other suitable locations within the gas turbine system. Humidity sensors 46, such as dry and wet thermometers, can measure ambient humidity in the inlet line of the compressor. Sensors 46 may also include flow sensors, velocity sensors, flame sensor sensors, valve position sensors, vane angle sensors, and other sensors typically used to measure different operating parameters and conditions associated with the operation of the gas turbine system 30. As used herein, the term "parameter" refers to measurable physical operating characteristics that may be used to define operating conditions within a system, such as a gas turbine system 30 or other generation system, as described herein. Operating parameters may include temperature, pressure, humidity, and gas flow characteristics at locations defined along the path of the working fluid, as well as environmental conditions, fuel characteristics, and other measurable characteristics as needed and without limitation. It will also be understood that the control system 31 also includes a plurality of actuators 47 by which it mechanically controls the operation of the gas turbine system 30. Actuators 47 may include electromechanical devices having variable setpoint inputs or settings that allow manipulation of certain process inputs (that is, manipulated variables) to control process outputs (ie, controlled variables) in accordance with a desired result or mode of operation. For example, commands generated by the component controller 31 may cause one or more actuators 47 within the turbine system 30 to adjust valves between the fuel supply and the combustor 34 that regulate the flow level, fuel splits, and / or type of combusted fuel. As another example, commands generated by the control system 31 may cause one or more actuators to adjust an inlet guide vane setting that changes its orientation angle.
The component controller 31 may be a computer system having a processor executing program code to control the operation of the gas turbine system 30 using sensor measurements and instructions from the user or factory operator ("operator 39" below). As discussed in more detail below, software executed by the controller 31 may include scheduling algorithms for regulating any of the subsystems described herein. The component controller 31 may partially regulate the gas turbine system 30 for algorithms stored in its digital memory. These algorithms may, for example, enable the component controller 31 to maintain the NOx and CO emissions in the turbine exhaust within certain predefined emission limits or, alternatively, to maintain the firing temperature of the combustion chamber within predefined limits. It is understood that algorithms may include inputs to parameter variables, such as compressor pressure ratio, ambient humidity, inlet pressure loss, turbine exhaust back pressure, and any other suitable parameters. The schedules and algorithms performed by component controller 31 take into account environmental variations affecting emissions, combustor dynamics, firing temperature limits at full and part load conditions, and so forth. As discussed in greater detail below, component controller 31 may employ algorithms to plan the gas turbine, such as those that set the desired turbine exhaust temperatures and combustor fuel splits, with the objective of meeting performance objectives while meeting gas turbine system operability limits. The component controller 31 may, for example, determine the increase in combustor temperature and NOx during part load operation to increase the operating margin to the limits of combustion dynamics and thereby improve the operability, reliability, and availability of the generating unit.
With reference to FIG. 3, a sketch of an exemplary power plant 12 having multiple generating units or power plant components 49 in accordance with aspects of the present invention is provided. The illustrated power plant 12 of FIG. 3 is a conventional configuration and is therefore used to discuss several of the exemplary embodiments of the present invention presented below. It will be understood, however, that the methods and systems described herein are more generally applicable and adaptable to power plants in scale having more generating units than those shown in FIG. 3, while still being applicable to power plants having a single generation Generating unit component such as those illustrated in FIG. 2. It is understood that the power plant 12 of FIG. 3 is a combined cycle plant having a plurality of power plant components 49, including a gas turbine system 30 and a steam turbine system 50. Power generation may be increased by other power plant components 49, such as an intake conditioning system 51 and / or a heat recovery steam generator having a pipe firing system (called "HRSG Pipe Lighting System 52" below). It will be appreciated that the gas turbine system 30, the steam turbine system 50 including the HRSG tube lighting system 52, and the intake conditioning system 51 each include a control system or component control device 31 that is in electronic communication with sensors 46 and actuators 47 associated with each factory component are. As used herein, unless otherwise stated, the intake conditioning system 51 may designate components that treat air prior to entering the compressor, for example, an intake cooling system or chiller, an evaporator, a fogger, water injection system, and / or, in some alternatives Cases, a heating element.
In operation, the intake conditioning system 51 cools the air entering the gas turbine system 30 such that the power generation capacity of the unit is increased. The HRSG tube lighting system 52 burns fuel to provide additional heat so as to increase the steam supply that is expanded by a turbine 53. Thus, the HRSG tube firing system 52 increases the energy delivered by the hot exhaust gases 55 from the gas turbine system and therefore increases the power generation capacity of the steam turbine system.
As an exemplary operation, the power plant 12 of FIG. 3 directs a fuel flow for combustion to the combustor 34 of the gas turbine system 30. The turbine 36 is powered by combustion gases and drives the compressor 32 and the generator 44 to supply electrical energy the transmission lines 14 of the power system 10 supplies. The component control device 31 of the gas turbine system 30 may specify commands for the gas turbine system related to the fuel flow rate and receive sensor data from the gas turbine system, such as intake air temperature, humidity, power output, shaft speed, and temperatures of the exhaust gas. The component controller 31 may also collect other operating data from pressure and temperature sensors, flow controllers, and other devices that monitor the operation of the gas turbine system. The component controller 31 may send data related to the operation of the gas turbine system and receive instructions from the power plant controller 22 in association with setpoint specifications for actuators that control the process inputs.
During certain modes of operation, the air entering the gas turbine system 30 may be cooled or otherwise conditioned by the intake conditioning system 51 to increase the generating capacity of the gas turbine system. The intake conditioning system 51 may include a cooling system 65 for cooling water and a component control device 31 that controls its operation. In this case, the component control device 31 may receive information related to the temperature of the cooling water as well as instructions related to the desired injection level that may come from the power plant controller 22. The component controller 31 of the inlet conditioning system 51 may also issue commands that cause the cooling system 65 to generate cooling water having a particular temperature and flow rate. The component controller 31 of the intake conditioning system 51 may send data pertaining to the operation of the intake conditioning system 51.
The steam turbine system 50 may include the turbine 53 and the HRSG tube lighting system 52, as well as a component control device 31 which, as illustrated, is dedicated to the control of its operation. Hot exhaust gases 55 from exhaust gas lines of the gas turbine system 30 may be directed into the steam turbine system 50 to produce the steam that is expanded through the turbine 53. It is understood that HRSG tube firing systems are in continuous use to provide additional energy for steam production to increase the generation capacity of a steam turbine system. It will be appreciated that the rotation induced by the steam within the turbine 53 drives a generator 44 to generate power that can then be sold within the power system 10 via transmission lines 14. The component control device 31 of the steam turbine system 50 may adjust the flow rate of the fuel burned by the tube firing device 52 and thereby increase the steam generation beyond the amount generated with the exhaust gases 55 alone. The component controller 31 of the steam turbine system 50 may send data pertaining to the operation of the factory component 49 and receive instructions as to how it should operate.
The power plant control device 22 of FIG. 3, as illustrated, may be connected to each component control device 31 and communicate via these connections with sensors 46 and actuators 47 of the plurality of power plant components 49. As part of controlling the power plant 12, the power plant controller 22 may simulate its operation. Specifically, the power plant controller 22 may include or communicate with digital models (or simply "models") that simulate the operation of each power plant component 49. The model may include algorithms that correlate process input variables with process output variables. The algorithms may include instruction sets, logic, mathematical formulas, function relationship descriptions, schedules, data collections, and / or the like. In the present case, the power plant control device 22 includes: a gas turbine model 60 that models the operation of the gas turbine system 30; an intake conditioning system model 61 that models the operation of the intake conditioning system 51, and a steam turbine model 62 that models the operation of the steam turbine system 50 and the HRSG tube lighting system 52. Generally, it will be understood that the systems and their associated models, as well as the individual steps of the methods provided herein, may be divided and / or combined in various ways without departing from the scope of the present invention, and the nature of their description will be exemplary except where: it is stated or claimed otherwise. Using these models, the power plant controller 22 may simulate the operation of the power plant 12, for example the thermodynamic power or parameters that describe the operation.
The power plant controller 22 may then use results from the simulations to determine optimized operating modes. Such optimized operating modes may be described by parameter sets having multiple operating parameters and / or setpoint specifications for actuators and / or other operating conditions. As used herein, the optimized mode of operation is one that is preferable to at least one alternative operating mode according to defined criteria or performance indicators that can be selected by an operator to assess factory operation. More specifically, optimized operating modes, as used herein, are those that are preferable to one or more other possible operating modes also simulated by the factory model. The optimized operating modes are determined by assessing how the model predicts how the power plant will operate under each operating mode. As discussed above, an optimizer 64, for example, a digital software optimization program, may execute the digital power plant model according to different parameter sets and then identify preferred or optimized operating modes by assessing the results. The variations of the setpoint inputs may be generated by disturbances applied around the setpoint selections selected for analysis. These can be partly based on historical operation. It is understood that the optimized mode of operation may be assessed by the optimizer 64 based on one or more defined cost functions. Such cost functions may include, for example, power generation cost, profitability, efficiency, or some other criteria as defined by operator 39.
To determine cost and profitability, the power plant control device 22 may include or be in communication with an economic model 36 that tracks the electricity price as well as other variable costs, such as the cost of the fuel in the gas turbine system, the intake conditioning system, and the HRSG Tube firing system is used. This business model 63 may provide data that the power plant controller 22 uses to judge which of the proposed setpoint defaults (that is, the selected setpoint defaults for which the optimized setpoint setpoint operation is modeled) represent minimum production cost or maximum profitability. According to other embodiments, as discussed in greater detail with FIG. 4, the optimizer 64 of the power plant controller 22 may include or may be connected to a filter, such as a Kalman filter, to assist in tuning, adjusting, and calibrating the digital models Models simulate the operation of the power plant 12 precisely. As discussed below, the model may be a dynamic model having a learning mode in that it compares or compares between actual operation (that is, values of measured operating parameters that reflect the actual operation of the power plant 12) and predicted operation (the means values for the same operating parameters that the model predicted). As part of the control system, the filter can also be used to set or calibrate the models in real time or near real time, such as every few minutes or hours, or as specified.
The optimized setpoint specifications generated by the power plant controller 22 are a recommended mode of operation and may include, for example, fuel and air settings for the gas turbine system, the temperature and water mass flow for the inlet conditioning system, the level of tube firing within the steam turbine system 50. According to certain embodiments, these proposed operational setpoint preferences may be provided to the operator 39 via a user interface device, such as via a computer screen, printer or speakers. Once he knows the optimized setpoint specifications, the operator may then input the setpoint inputs to the power plant control device 22 and / or the component control device 31, which then generates control information to achieve the recommended operating mode. In those embodiments where optimized setpoint inputs do not include specified control information for implementing the operating mode, the component control devices may provide the required control information therefor, as discussed in more detail below, and continue to control the power plant component in the closed loop fashion according to the recommended operating mode until the next optimization cycle , Depending on the preferences of the operator, the power plant control device 22 may also implement directly or automatically optimized setpoint specifications without the involvement of the operator.
As an exemplary operation, the power plant 12 of FIG. 3 directs a fuel stream for combustion to the combustor 34 of the gas turbine system 30. The turbine 36 is powered by combustion gases and drives the compressor 32 and the generator 44 to supply electrical energy the transmission lines 14 of the power system 10 supplies. The component controller 31 may set commands for the gas turbine system 30 related to the fuel flow rate and receive sensor data from the gas turbine system, such as intake air temperature, humidity, power output, shaft speed, and temperatures of the exhaust gas. The component controller 31 may also collect other operating data from pressure and temperature sensors, flow controllers, and other devices that monitor the operation of the gas turbine system 30. The component controller 31 of the gas turbine system 30 may send data related to the operation of the system and receive instructions from the power plant controller 22 in association with setpoint specifications for actuators that control the process inputs.
During certain modes of operation, the air entering the gas turbine system 30 may be cooled by cold water supplied to the intake air line 42 from the intake conditioning system 51. It will be appreciated that the cooling of the air entering a gas turbine may occur to increase the capacity of the gas turbine engine to generate power. The intake conditioning system 51 includes a cooling system or cooler 65 for cooling water and a component control device 31. In the present case, the component control device 31 receives information related to the temperature of the cooling water and commands related to the desired cooling of the intake air. These commands may come from the power plant controller 22. The component controller 31 of the inlet conditioning system 51 may also issue commands that cause the cooling system 65 to generate cooling water having a particular temperature and flow rate. The component controller 31 of the inlet conditioning system 51 may send data related to the operation of the inlet conditioning system 51 and receive instructions from the controller 22.
The steam turbine system 50 may include a HRSG having a tube firing device 52, a steam turbine 53, and a component control device 31 dedicated to this operation. Hot exhaust gases 55 from an exhaust conduit 42 of the gas turbine system 30 are directed into the steam turbine system 50 to generate the steam that drives it. The HRSG tube lighting system 52 may be used to provide additional heat energy to generate steam to increase the generating capacity of the steam turbine system 50. The steam turbine 53 drives the generator 44 to generate power that is supplied to the power system 10 via the transmission lines 14. The component control device 31 of the steam turbine system 50 may adjust the flow rate of the fuel burned by the tube firing device 52. Heat generated by the tube firing device increases the generation of vapor beyond the amount generated by the exhaust gases 55 from the turbine 36 alone. The component controller 31 of the steam turbine system 50 may send data related to the operation of the system and receive instructions from the power plant controller 22.
The power plant controller 22 may communicate with the operator 39 and data sources 26 to receive, for example, market conditions data, such as prices and demand for the power supplied. In certain embodiments, the power plant controller 22 issues recommendations to the operator 39 in connection with desired operating setpoint specifications for the gas turbine system 30, the intake conditioning system 51, and the steam turbine system 50. Power plant controller 22 may receive and store data for operating the components and subsystems of power plant 12. Power plant controller 22 may be a computer system having a processor and memory storing data, digital models 60, 61, 62, 63, optimizer 64, and other computer programs. The computer system may be embodied in a single physical or virtual computing device or distributed to local or remote computing devices. The digital models 60, 61, 62, 63 may be embodied as a set of algorithms, for example, transfer functions related to operating parameters of each of the systems. The models may include a physics-based aerothermodynamic computer model, a regression model, or other suitable computer-implemented model. According to preferred embodiments, the models 60, 61, 62, 63 may be tuned, adjusted or calibrated regularly, automatically and in real time or in near real time, or tuned according to ongoing comparisons between predicted operation and measured parameters of actual operation. Models 60, 61, 62, 63 may include filters that receive data inputs related to the combined cycle's current physical and thermodynamic operating conditions. These data inputs may be provided to the filter in real time or periodically every 5 minutes, 15 minutes, hours, days, etc. during operation of the power plant 12. The data inputs may be compared to data predicted by the digital models 60, 61, 62, 63 and, based on the comparisons, the models may be continually refined.
FIG. 4 illustrates a schematic configuration system of a power plant controller 22 having a filter 70, an artificial neural network configuration 71 ("neural network 71"), and an optimizer 64 in accordance with aspects of the present invention. The filter 70, which may be, for example, a Kalman filter, may present the current data 72 of the measured operating parameters from sensors 46 of the power plant 12 with predicted data 73 of the same operating parameters by the models 60, 61, 62, 63 and the neural network 71 including the Operation of the power plant 12 simulated, compare. Differences between the current data and the predicted data may be used by the filter 70 to tune the model of the power plant simulated by the neural network 71 and the digital models.
It should be understood that while certain aspects of the present invention are described herein with reference to models in the form of models based on a neural network, it is to be understood that the present invention may be practiced using other types of models including, but not limited to, models based on physics, models driven by data, models developed empirically, models based on heuristics, support vector machine models, models developed by linear regression, Models that are developed using "rationale" knowledge, etc. In order to properly capture the relationship between the manipulated / perturbing variables and the controlled variables, the power plant model according to certain preferred embodiments may additionally have one or more of the following features: 1) Nonlinearity (a nonlinear model is capable of producing a K rather than a straight-line relationship between manipulated / interfering and controlled variables); 2) multiple input / multiple output (the model may be able to detect relationships between multiple inputs - the manipulated and confounding variables - and multiple output-controlled variables); 3) Dynamics (changes in input can not affect output immediately, there may instead be a time delay followed by a dynamic response to the changes, for example, it may take several minutes for input changes to propagate completely through the system) , Since optimization systems are executed at a predetermined frequency, the model must account for and consider the effects of these changes over time), 4) adaptive (the model can be updated at the beginning of each optimization to reflect the current operating conditions); and 5) derived from empirical data (since each power plant is unique, the model can be derived from empirical data obtained from the power generation unit). In view of the above requirements, a neural network based approach is a preferred technology for implementing the required factory models. Neural networks can be developed based on empirical data using higher regression algorithms. It will be understood that neural networks are capable of detecting the nonlinearity that is usually presented in the operation of the power plant components. Neural networks can also be used to represent systems with multiple inputs and outputs. In addition, neural networks can be updated using either feedback bias or adaptive online learning. Dynamic models can also be implemented in a structure based on neural network. A variety of different model architecture types have been used to translate dynamic neural networks. Many of the neural network model architectures require a large amount of data to successfully train the dynamic neural network. For a robust power plant model, it is possible to calculate the effects of changes in the manipulated variables on the controlled variables. Further, since the factory model is dynamic, it is possible to calculate the effects of manipulated variable changes during a future time horizon.
The filter 70 may generate power multipliers applied to inputs or outputs of the digital models and the neural network, or the weights applied to the logic units and algorithms used by the digital models and the neural network. These actions through the filter reduce the differences between the actual condition data and the predicted data. The filter continues to work to further reduce the differences or to deal with any fluctuations that may occur. By way of example, the filter 70 may generate power multipliers for the predicted data associated with the compressor outlet pressure and temperature in the gas turbine, the efficiency of the gas and steam turbines, fuel flow to the gas turbine system, inlet conditioning system, and HRSG tube firing system, and / or other suitable parameters. It is understood that these categories of operating data reflect operating parameters that are subject to performance degradation over time. By providing power multipliers for these data types, the filter 70 may be particularly useful in setting the models and the neural network to account for the degradation in power plant performance.
As illustrated in FIG. 4, according to certain embodiments of the present invention, each of the digital models 60, 61, 62, 63 of the plurality of power plant components 49 of the power plant of FIG. 3 includes algorithms represented by a plurality of graphics using be used to model the corresponding systems. The models interact and communicate within the neural network 71, and it is understood that the neural network 71 thereby forms a model of the entire combined cycle power plant 12. Thus, the neural network simulates thermodynamics and economic operation of the plant. As indicated by the solid arrows in Fig. 4, the neural network 71 collects data output from models 60, 61, 62, 63 and provides data to be used as inputs from the digital models.
The power plant control device 22 of FIG. 4 also includes an optimizer 64, such as a computer program interacting with the neural network 71, for optimum setpoint specifications for the gas turbine system, intake conditioning system, steam turbine system, and HRSG tube lighting system to research to get a defined performance objective. For example, the performance objective may consist in maximizing the profitability of the power plant. The optimizer 64 may cause the neural network 71 to execute the digital models 60, 61, 62, 63 with different operational setpoints. The optimizer 64 may have perturbation algorithms that assist in varying the setpoint specifications of the models. The perturbation algorithms cause the simulation of the combined cycle power plant provided by digital models and neural network to operate with setpoint specifications that are different from the current setpoint specifications for the plant. By simulating the plant's operation with different setpoint inputs, the optimizer 64 searches for operating setpoint defaults that would cause the plant to operate more efficiently or improve performance by some other criterion defined by the operator 39.
[0037] According to example embodiments, the business model 63 provides data that the optimizer 64 uses to determine which setpoint defaults are most profitable. For example, the business model 63 may receive and store cost data formatted such that a table 630 that correlates fuel cost over time, such as during the seasons of a year, may be used. Another table 631 may correlate the price taken for power at different times of the day, weeks, or months. Economic model 63 may provide data related to the price received for electricity and the fuel costs (gas turbine fuel, tube firing fuel, and intake conditioning system fuel) used for its production. The data from the business model 63 may be used by the optimizer 64 to assess all power station operating states according to power objectives defined by the operator. The optimizer 64 may identify which of the operating states of the power plant 12 is optimal in view of the performance objectives defined by the operator 39 (which, as used herein, is preferable at least compared to an alternative operating state). As described, the digital models may be used to simulate the operation of the power plant components 49 of the power plant 12, such as modeling the thermodynamic operation of the gas turbine system, the intake conditioning system, or the steam turbine system. The models may include algorithms, such as mathematical equations and look-up tables, which may be stored locally and updated at regular intervals or remotely obtained via data resources 26 that simulate the reaction of the power plant component 49 to specific input conditions. Such look-up tables may include measured operating parameters describing the operation of the same type of components as those operating on remote power plants.
The heat model 60 of the gas turbine system 30 includes, for example, an algorithm 600 that correlates the effect of the temperature of the intake air with the power output. It is understood that this algorithm can show that the current output decreases from a maximum value 601 as the inlet temperature rises above a threshold temperature 602. The model 60 may also include an algorithm 603 that correlates the heat input coefficient of the gas turbine to different power output levels of the machine. As discussed, the heat input coefficient represents the efficiency of a gas turbine engine or other power generation unit and is inversely related to efficiency. A lower heat input coefficient indicates a higher thermodynamic power efficiency. The digital model 61 may simulate the thermodynamic operation of the intake conditioning system 51. In this case, the digital model 61, for example, has an algorithm 610 that correlates the cooling capacity based on energy used to operate the cooling system 65 of the intake conditioning system 51 such that the calculated cooling capacity estimates the amount of cooling that occurred indicates the air entering the gas turbine indicates. There may be a maximum cooling capacity value 611 that can be obtained by the cooling system 65. In another case, an associated algorithm 612 may correct the energy used to operate the cooling system 65 with the temperature of the cooled air entering the compressor 32 of the gas turbine system 30. The model 61 may, for example, show that the power required to operate the intake conditioning system increases dramatically as the temperature of the input air entering the gas turbine falls below the dew point 613 of the ambient air. In the case of the steam turbine system 50, the digital model 62 may include an algorithm 620 that correlates the current output of the steam turbine system with the energy added by the HRSG tube lighting system 52, such as the amount of fuel consumed by tube firing. The model 22 may indicate, for example, that there is an upper threshold 621 for increasing the steam turbine system output that may be obtained by the HRSG tube lighting system that may be included in the algorithm 620.
According to certain embodiments of the present invention, as illustrated in FIG. 4, the neural network 71 may interact with the digital models of multiple plant components 49 of the power plant 12 of FIG. 3 and provide communications between them. The interaction may include collecting output data from the models and generating input data used by the models to generate further output data. The neural network 71 may be a digital network of connected logic elements. The logic elements may each embody an algorithm that accepts data inputs to generate one or more data outputs. A simple logic element can sum the values of the inputs to produce output data. Other logical elements can multiply values of the inputs or apply other mathematical relationships to the input data. The data inputs to each of the logic elements of the neural network 71 may be assigned a weight, such as a multiplier between one and zero. The weights may be modified during a learning mode that adjusts the neural network to better model the power plant's performance. The weights may also be adjusted based on commands provided by the filter. Setting the weights of the data inputs to the logic units in the neural network is an example of how the neural network can be changed dynamically during operation of the combined cycle power plant. Other examples include changing weights of the data inputs to algorithms (which are an example of a logic unit) in each of the thermodynamic digital models for the steam turbine system, intake conditioning system, and the gas turbine. The power plant controller 22 may be changed in other ways, such as by adjustments to logic units and algorithms based on the data provided by the optimizer and / or filter.
The power plant controller 22 may generate an output of recommended or optimized setpoint inputs 74 for the combined cycle power plant 12 that, as illustrated, may pass through an operator 39 for approval before communicating with and being converted by the power plant actuators 47. As illustrated, the optimized setpoint inputs 74 may include inputs via a computer system, such as that described below in connection with FIG. 6, entered or approved by the operator. The optimized setpoint inputs 74 may include, for example, a temperature and mass flow rate for the cooling water generated and used by the intake conditioning system to cool the air entering the gas turbine system and a fuel flow rate to the gas turbine system, and a pipe firing rate. It will be appreciated that the optimized setpoint inputs 74 may also be used by the neural network and 70 and the models 60, 61, 62, 63 such that the ongoing power plant simulation may predict operating data which may later be compared to actual operating data the power plant model can be continuously refined.
FIG. 5 illustrates a simplified system configuration of a power plant controller 22 having an optimizer 64 and a power plant model 75. In this exemplary embodiment, the power plant control device 22 is shown as a system having the optimizer 64 and the power plant model 75 (e.g., the neural Network 71 and the models 60, 61, 62, 63 discussed above in connection with FIG. 4). The power plant model 75 may simulate the operation of a power plant 12 as a whole. According to the illustrated embodiment, the power plant 12 has multiple generating units or power plant components 49. Power plant component 49 may include, for example, heat generation units or other power plant subsystems that have already been described, each of which may include corresponding component control devices 31. The power plant control device 22 may communicate with the component control devices 31 and control the operation of the power plant 12 through and with the component control devices 31 via connections to sensors 46 and actuators 47.
It is understood that power plants have numerous variables that affect their operation. Each of these variables can generally be classified as either an input variable or output variable. Input variables represent process inputs and include variables that can be manipulated by power plant operators, such as air and fuel flow rates. The input variables also include those variables that can not be manipulated, such as environmental conditions. Output variables are variables, such as a current output, that can be controlled by manipulating the input variables that can be manipulated. A power plant model is configured to provide the algorithmic relationship between input variables that include those that can be manipulated, or "manipulated variables," and those that can not be manipulated, or "noise variables," and output or controlled variables that are "controlled." Variables ». Specifically, manipulated variables are those that can be varied by the power plant controller 22 to affect controlled variables. Manipulated variables have things like valve setpoints that control fuel and airflow. Interference variables refer to variables that influence controlled variables but can not be manipulated or controlled. Interference variables have environmental conditions, fuel characteristics, etc. Optimizer 64 determines an optimal set of manipulated variable setpoints in view of: (1) power plant performance goals (eg, meeting load requirements while maximizing profitability), and (2) constraints associated with power plant operation are (for example, emission and equipment restrictions) on.
According to the present invention, an "optimization cycle" may begin at a predetermined frequency (for example every 5 to 60 seconds or every 1 to 30 minutes). At the beginning of an optimization cycle, the power plant controller 22 may receive existing data for manipulated variables, controlled variables and disturbance variables from the component control devices 31 and / or directly from sensors 46 of each of the power plant components 49. The power plant controller 22 may then use the power plant model 75 to determine optimal set values for the manipulated variables based on the present data. Power plant controller 22 may execute power plant model 75 with different operating setpoint defaults to determine which set of operating setpoints is most preferred in view of power plant performance objectives, which may be termed "simulation runs". For example, a performance objective may be maximizing profitability. By simulating the plant's operation with different setpoint inputs, the optimizer 64 searches for the set of setpoint inputs that the power plant model 75 predicts will cause the power plant to operate in an optimal (or at least preferred) manner. As indicated, this optimum set of setpoints may be called "optimized setpoints" or an "optimized mode of operation". Typically, optimizer 64, when it comes to the optimized setpoints, has compared numerous sets of setpoints, and it will be noted that the optimized setpoints are higher than any of the other sets given the performance objectives defined by the operator. The operator 39 of the power plant 12 may have the option to approve the optimized setpoints, or the optimized setpoints may be automatically approved. The power plant controller 22 may send the optimized setpoints to the component controller 31 or, alternatively, directly to the actuators 47 of the power plant components 49 so that adjustments may be made according to the optimized setpoints. The power plant controller 22 may be operated in a closed loop to set target values of the manipulated variables at a predetermined frequency (for example, every 10 to 30 seconds or more) based on the measured current operating conditions.
The optimizer 64 may be used to minimize a "cost function" that is subject to a set of constraints. The cost function is essentially a mathematical representation of a power plant performance objective, and the limitations are limits within which the power plant must operate. Such limits may be statutory, regulatory, environmental, equipment or physical limitations. For example, to minimize NOx, the cost function has a term that decreases as the NOx level decreases. A common method for minimizing such a cost function is known, for example, as the "gradient method". The gradient method is an optimization algorithm that approximates a local minimum to a function by taking steps proportionate to the negative side of the gradient (or approximate gradient) of the function at the current point. It will be understood that a number of different optimization techniques may be used depending on the shape of the model and the cost and constraints. For example, it is contemplated that the present invention may be practiced by using, alone or in combination, a variety of different types of optimization approaches. These optimization approaches include, but are not limited to, linear programming, quadratic programming, mixed integer nonlinear programming, stochastic programming, global nonlinear programming, genetic algorithms and particle / swarm techniques. In addition, the power plant model 75 may be dynamic so that effects of changes over a future time horizon are taken into account. The cost function therefore has links over a future horizon. Since the model is used to predict over a time horizon, this approach is called the model predictive control described in S. Piche, B. Sayyar-Rodsari, D. Johnson and M. Gerules, "Nonlinear model predictive control using neural networks", IEEE Control Systems Magazine, Vol. 20, No. 2, pp. 53-62, 2000 and incorporated herein by reference in its entirety.
Restrictions may be imposed on both the process inputs (including the manipulated variables) and process outputs (including controlled variables) of the power plant over the future time horizon. Typically, constraints that are consistent with limits associated with the power plant controller are imposed on the manipulated variables. Restrictions on spending can be determined by the problem being solved. In accordance with embodiments of the present invention, and as a step in the optimization cycle, the optimizer 64 may calculate the complete trajectory of manipulated variable motions over the future time horizon, for example one hour. Thus, for an optimization system running every 30 seconds, 120 values can be calculated over a one-hour future time horizon for each manipulated variable. Because the power plant model or performance objectives or constraints may change prior to the next optimization cycle, the power plant controller 22 / optimizer 64 may output only the first value in the time horizon for each manipulated variable to the component control devices 31 as optimized setpoints for each manipulated variable. At the next optimization cycle, the power plant model 75 may be updated based on the current conditions. The cost function and limitations can be updated if they have changed. The optimizer 64 may then be used to recompute the set of manipulated variable values over the time horizon, and the first value of the time horizon for each manipulated variable is output to the component controller 31 as the setpoint for each respective manipulated variable. The optimizer 64 may repeat this process for each optimization cycle, thereby constantly maintaining optimum performance, while the power plant 12 is affected by unexpected changes to elements such as load, environmental conditions, fuel characteristics, and so forth.
Referring now to FIG. 6, an illustrative environment and user input device for a power plant controller and control program are illustrated in accordance with an exemplary embodiment. Although other configurations are possible, the embodiment includes a computer system 80 having a display 81, a processor 82 and a user input device 83, and a memory 84. Aspects of the computer system 80 may reside at the power plant 12 while other aspects may be removed or connected via the communications network 20. As discussed, the computer system 80 may be connected to each generating unit or other power plant component 49 of the power plant 12. The power plant components 49 may include the gas turbine system 30, the steam turbine system 50, the intake conditioning system 51, the HRSG tube lighting system 52, and / or other subsystems or subcomponents associated therewith or any combination thereof. The computer system 80 may also be connected to one or more sensors 46 and actuators 47 as needed or desired. As mentioned, the sensors 46 may be configured to detect operating conditions and parameters of the components and pass signals to the computer system 80 in association with those conditions. The computer system 80 may be configured to receive these signals and to use them in manners described herein which include transmitting signals to one or more of the actuators 47. However, unless required, the present invention may include embodiments that are not configured to directly control the power plant 12 and / or detect operating conditions. In configurations of the present invention that control the power plant 12 and / or detect operating conditions, such input or control may be provided by receiving and / or transmitting signals from / to one or more separate software or hardware systems that interface directly with physical components of the system Power station and its sensors and actuators interact. The computer system 80 may include a power plant control program ("control program") that causes the computer system 80 to operate to manage data in a power plant control device by executing the processes described herein.
In general, processor 82 executes program code that defines the control program that is at least partially committed in memory 84. While executing the program code, the processor 82 may process data, which may result in reading and / or writing transformed data from / to the memory 84. The display 81 and input device 83 may enable a human user to interact with the computer system 80 and / or with one or more communication devices to enable a system user to communicate with the computer system 80 using any type of communication links. In embodiments, a communication network, such as network hardware / software, may enable computer system 80 to communicate with other devices inside and outside a node in which it is installed. To this end, the control program of the present invention may manage a set of interfaces that enable human and / or system users to interact with the control program. The control program, as discussed below, may also manage data (eg, store, fetch, create, manipulate, organize, present, etc.), such as control data, using any solution.
Computer system 80 may include one or more general purpose computing products capable of executing program code, such as the control programs defined herein, installed thereon. As used herein, "program code" means any collection of instructions in any language, code or notation that cause a computing device having data processing capability to perform a particular action either directly or as a combination of the following: (a) Conversion to another language, code or notation; (b) in a different material form; and / or (c) decompress. Additionally, computer code may include object code, source code, and / or executable code and form part of a computer program product when residing on a computer-readable medium. It is understood that the term "computer readable medium" may include one or more of any type of tangible expression media now known or later developed, from which a copy of the program code may be perceived, reproduced, or otherwise communicated by a computing device. When the computer executes the computer program code, it becomes an apparatus for applying the invention, and on a general-purpose microprocessor, specific logic circuits are created by configuring the microprocessor with computer code segments. A technical effect of the executable instructions is to implement a power plant control method and / or system and / or computer program product that uses models to improve or enhance or optimize operational characteristics of power plants to improve the economic viability of a power plant in the face of expected environmental and / or environmental conditions Market conditions, performance parameters and / or lifecycle costs associated therewith. In addition to using up-to-date information, historical and / or predicted information may be used, and a feedback loop may be established to efficiently dynamically operate the power plant during fluctuating conditions. The computer code of the control program may be written in computer instructions that may be executed by the power plant controller 22. For this, the control program executed by the computer system 80 may be embodied as any combination of system software and / or application software. The control program may also be implemented using a set of modules. In this case, a module may enable the computer system 80 to perform a set of tasks used by the control program and may be developed separately and / or implemented separately from other portions of the control program. As used herein, the term "component" means any configuration of hardware, with or without software, that implements the functionality described in connection therewith, in which any solution is used, while the term "module" means program code, enabling a computer system to implement the actions described in connection therewith using any solution. When committed in the memory 84 of the computer system 80 having the processor 82, a module is an essential portion of a component that implements the actions. Nevertheless, it is understood that two or more components, modules, and / or systems may share some / all of their respective hardware and / or software. Further, it should be understood that some of the functionality discussed herein may not be implemented or additional functionality may be included as part of the computer system 80. If the computer system 80 includes multiple computing devices, each computing device may have only a portion of the control program committed to it (eg, one or more modules). Nevertheless, if the computer system 80 includes multiple computing devices, the computing devices may communicate over any type of communication link. While performing a process described herein, computer system 80 may also communicate with one or more other computer systems using any type of communication link.
As discussed herein, the control program enables the computer system 80 to implement a power plant control product and / or method. Computer system 80 may receive power plant control data using any solution. For example, the computer system 80 may generate and / or use power plant control data, generate power plant control data from one or more data stores, inventories or sources, power plant control data from any other system, or any other device inside or outside of the power plant, from the power plant control device, component control device and / or the like. In another embodiment, the invention provides a method of providing program code copy, such as for power plant control programs, that may implement some or all of the processes described herein. It should be understood that aspects of the invention may be practiced as part of a business methodology that executes a process described herein on a subscription, advertising, and / or fee basis. A service provider might offer to implement a power plant control program and / or method as described herein. In this case, the service provider may manage (eg, create, maintain, support, etc.) a computer system, such as computer system 80, which executes a process described herein for one or more customers.
Computer models of power plants can be constructed and then used to control and optimize power plant operation. Such power plant models may be dynamic and iteratively updated via ongoing comparison between current (ie, measured) operating parameters and the same parameters as predicted by the power plant model.In preparing and maintaining such models, instructions may be written or otherwise provided that instruct the processor 82 of the computer system 80 to generate a library of power system generation units and components ("component library") in response to user input. In some configurations, the user input and the generated library have properties of the component with the library as well as rules to generate scripts in accordance with operational and property values. These property values may be compiled from data stored locally in memory 84 and / or taken from a central database maintained at a remote location. The component library can not have physical components, such as economic or legal components. Examples of economic components are fuel purchases and sales, and examples of legal components are emission limits and credit. These non-physical components can be modeled using mathematical rules, just as components that represent physical equipment can be modeled using mathematical rules, for example. The instructions may be configured to assemble a configuration of power system components from the library, as may be configured by an operator. A library of power system components may be provided such that a user may select components therefrom to replicate an actual power plant or to create a hypothetical one. It is understood that each component may have multiple properties that may be used by the user to input specific values that correspond to operating conditions of an actual or hypothetical power plant being modeled. Scripts can be generated for the merged power system components and their configuration. The generated scripts may include mathematical relationships within and / or among the power system components, including economic and / or legal components, if used in the power system component configuration. The computer system 80 may then solve mathematical relationships and show results of the solution on the display 81. In configurations where signals can be transmitted from the computer 80, the signals may be used to control a power system in accordance with the results of the solution. Otherwise, results may be displayed or printed and used to set physical equipment parameters and / or determine and / or use specified non-physical parameters, such as fuel purchases and / or sales, to achieve a preferred or optimized operating mode. The power plant component library may have a central database that represents an ongoing accumulation of data associated with how each power plant component operates under different parameters and conditions. The central database can be used to provide "plug data" for cases where sensor data is considered unreliable.
[0051] Referring to Figures 7 through 9, there is provided a more detailed discussion of the economic allocation process, including ways and means, as discussed above, of optimizing such allocation procedures from the perspective of both a central power system authority or individual power plants. who participate in such systems can be used. It is understood that, from the point of view of a central governmental arbitrator, the objectives of the economic allocation process are to dynamically respond to changing variables, including changing load requirements or environmental conditions, while minimizing generation costs within the system. For the participating power plants, it is understood that the objective generally is to use available capacity while minimizing generation costs so as to maximize profitability. Given the complexities of power systems, the process of economic allocation typically involves frequent metering of the load on the participating power plants. If successful, the process results in operating available power plants with loads in which their incremental generation costs are about the same, resulting in minimizing generation costs while also taking into account system constraints, such as maximum and minimum allowable loads, System stability, etc. It is understood that accurate incremental cost data is required for optimal economic allocation. Such incremental cost data has primary components that have fuel costs and incremental fuel consumption. The incremental fuel consumption data is usually given as a graph of incremental heat cost coefficients compared to the current output. Specifically, the incremental heat input coefficient, IHR, of a heat generation unit is defined as the slope of the heat expenditure coefficient curve, the unit heat expenditure coefficient representing the ratio of the input heat as compared to the current output at any load. Errors in these data result in allocating units at loads that do not minimize the overall production cost.
A number of elements may introduce errors into the incremental heat load coefficient curves. These can be grouped into two categories. A first category contains elements that generate errors that exist at the time the data is given to the dispatcher. For example, when the data is collected through testing, errors due to precision deficiencies of the meters are included in all calculations performed on them. As discussed in more detail below, certain aspects of the present invention provide means to confirm sensor precision during data collection and to timely identify instances in which collected data may be unreliable due to sensor malfunctions. A second category of errors contains elements that make data less accurate over time. For example, if the performance of a generation unit changes due to equipment degradation or repair or changes in environmental conditions, the incremental heat management coefficient data used for the allocation is incorrect until the data is updated. One aspect of the present invention is to identify those parameters of heat generation units that significantly affect the calculation of incremental heat expenditure coefficients. Knowledge of such parameters and their relative significance can then be used to determine how often allocation data should be updated to reflect the actual power of the power plant.
Errors in the incremental heat load coefficient data lead to situations where power plants are misallocated, which typically results in increased power system generation costs. For example, referring to the graph of FIG. 7, there is provided a situation where the true incremental heat load coefficient is different than the incremental heat load coefficient used in the allocation process. When allocating the unit, the dispatching authority uses the incremental heat expenditure coefficient data that is erroneous by "E" as indicated. (It should be noted that FIG. 7 assumes that the incremental thermal effort coefficient of a power system is not affected by the load imposed on the given unit, which may be substantially correct if the power system is compared to the size of the given power system Generating unit is a large one.) As shown, the generating unit is allocated to Li, namely, the load at which the unit and system's incremental heat input coefficients are equal based on the available information. If the correct heat load coefficient information were used, the unit would be allocated at L2, the load at which the actual heat load coefficient of the power plant is equal to the incremental heat load coefficient of the power system. It is understood that the error results in underutilization of the power plant. In cases where the alternative is true, that is, where the positioning of the plotter of the incorrect incremental heat input coefficient is inverted compared to the actual incremental heat input coefficient, the error results in the unit being over-committed, which may require it to be inefficient works to cover their assigned load commitment. From the point of view of the central dispatching authority of the power system, it is understood that reducing errors in the data used in the dispatching process reduces the fuel cost of the entire system, increases system efficiency, and / or reduces the risk of failure to meet load demands. For the operators of power plants within the system, reducing such errors should promote full utilization of the power plant and improve profitability.
FIGS. 8 and 9 respectively illustrate a sketch of a power plant control device 22 and a flowchart 169 of a control method according to aspects of the present invention. In these examples, methods are provided that illustrate economic optimization within a power system that utilizes economic allocation to distribute load to potential providers. The basic process of economic allocation is one that can be used in different ways and between any two levels defined within the multi-level hierarchy that many power systems have in common. In one case, for example, the economic allocation process may be used as part of a competitive process whereby a central government agency or industry association distributes load among several competing companies. Alternatively, the same principles of economic allocation may be used to divide load among co-owned power plants in order to minimize generation costs for power plant owners. They may also be used at power plant level as a way for the operator or power plant control device to split its load requirements to different local generating units that are available for it. It will be understood that unless otherwise stated, the systems and methods of the present invention are generally applicable to all of these possible manifestations of the economic allocation process.
In general, the allocation process tends to minimize generation costs within a power system by applying allocation scheduling in which the incremental generation cost is about the same for each participating power plant or unit involved. It is understood that several terms are often used to describe the economic allocation process, and it is therefore defined as follows. A "prediction horizon" is a predetermined period of time during which optimization is to be performed. For example, a typical forecast horizon may be a few hours to a few days. An "interval" within the prediction horizon is a predefined optimization time resolution, that is to say the "optimization cycle" mentioned above, which describes how often optimization is to be carried out during the prediction horizon. For example, a typical time interval for an optimization cycle may be several seconds to several minutes. Finally, a "prediction length" is the number of time intervals for which optimization is to be performed and can be obtained by dividing the prediction horizon by the time interval. For a 12-hour forecast horizon and a 5-minute time interval, the prediction length is 144 time intervals.
Aspects of the present invention provide control methods and / or control devices for power plants as well as methods and systems for optimizing performance, cost-effectiveness and efficiency. According to the present invention, for example, a minimum of variable operating costs for a heat generating unit or power plant may be achieved, including variable performance and cost parameters (ie fuel costs, environmental conditions, market conditions, etc.) with life cycle costs (i.e., variable operation and its impact on Maintenance planning, parts replacement, etc.) compensates. By varying one or more parameters of a heat generating unit taking into account such factors, the unit can be used more economically during its service life. For example, in power plants having a gas turbine, the firing temperature may be varied to provide a desired load level that is more economically based on operating profile, environmental conditions, market conditions, forecasts, power plant performance, and / or other factors. As a result, the disposal of parts with life-time based on remaining hours that remain in drive-limited units can be reduced. Further, a power plant control system having a feedback loop that is updated with substantially real-time data from sensors that are periodically tested and confirmed to be functioning properly allows further power plant optimization. Therefore, in accordance with certain embodiments of the present invention, by introducing a real-time feedback loop between the power plant control system and the dispatching authority, target loads and unit commitment may be based on high-precision supply curves that are assembled based on real-time engine performance parameters.
FIG. 8 illustrates a schematic of an exemplary power plant controller 22 in accordance with aspects of the present invention. It is understood that the power plant control device 22 may be particularly well suited for implementing the method 169 of FIG. 9. Thus, FIGS. 8 and 9 are discussed together, although it will be understood that any of them may have aspects that are more generally applicable. Power system 10, shown in FIG. 8, includes a "power plant 12a" to which power plant controller 22 is associated, and "other power plants 12b" that may represent power plants within the power system that are in competition with power plant 12a. As illustrated, the power system 10 also includes an arbitration authority 24 that, through a dedicated system controller 25, manages the arbitration process among all participating power plants 12a, 12b within the system.
The power plant 12a may include a plurality of sensors 46 and actuators 47, on the basis of which the power plant control device 22 monitors operating conditions and controls the operation of the power plant. Power plant controller 22 may communicate with numerous data sources 26 that may be remote therefrom and accessible via a communications network and / or may be locally contained and accessible via a local area network. As illustrated, the schematic representation of the power plant control device 22 includes several subsystems that have been demarcated from one another by a plurality of boxes. These subsystems or "boxes" were mostly separated by function to help with the description. It should be understood, however, that separate boxes may or may not represent individual chips or processors or other discrete hardware elements and may or may not depict separate portions of computer program code executed within the power plant control device unless otherwise noted. Similarly, although the method 169 is divided into two main sections or blocks, this has a practical purpose and is intended to aid in the description. It will be appreciated that any or all of the separate boxes shown in FIG. 8 may be combined into one or more sections in the power plant controller 22, as well as any or all of the separate blocks or steps shown in FIG are.
For example, the method 169 of FIG. 9 may begin with a control section 170 that receives or collects existing information and data for use (at step 171) that may include market data, operational data, and / or environmental data. Within the power plant controller 22, a corresponding control module 110 may be arranged to request / receive this type of data from data sources 26 or any other suitable sources. The control module 110 may also be configured to receive a destination load 128 from the dispatching authority 24 (although such a target load may not be available at an initial run) and a predefined initial target load may be used.Environmental data may be received from remote or local databases and / or forecasting services and may be included as a component of the data sources 26. Environmental data may also be collected via environmental sensors distributed around the power plant 12a as well as received via a communication link with the dispatching authority 24. In accordance with aspects of the present invention, environmental data includes historical, present, and / or predicted data describing environmental conditions for the power plant 12a, which may include, for example, air temperature, relative humidity, pressure, and so on. Market data may be received from remote or local databases and / or forecasting services and may be included as a component of the data sources 26. Market data may also be received via a communication link with the dispatching authority 24. In accordance with aspects of the present invention, market data includes historical, present, and / or forecast data describing market conditions for the power plant 12a, which may include, for example, electricity sales prices, fuel costs, labor costs, and the like. Operating data may also be received from databases and / or forecasting services and may be included as a component of data sources 26. Operating data may include data collected by a plurality of sensors 46 distributed within the power plant 12 and its power plant components 49 that measure physical parameters associated with power plant operation. The operational data may include historical, present, and / or forecast data, as well as a variety of process inputs and outputs.
As seen in FIG. 9, an initial target value for the power plant 12 may be determined, such as with a control model 111 in the power plant control device 22 of FIG. 8. For example, the control model 111 may be configured to use thermodynamic and / or physical details of the power plant 12 and additional information, such as environmental data or market data or process data, to determine a value of an operating parameter for the power plant 12 (at step 172 of FIG Fig. 9). For example, in one case, the value of an operating parameter may be a value that would be required to achieve current output sufficient to meet a target load. The particular value may be used as an initial set point for the particular operating parameter of the power plant 12 (also step 172 of FIG. 9). It is understood that examples of such operating parameters may include: fuel flow rate, firing temperature, position for inlet guide vanes (if vanes are present), vapor pressure, vapor temperature, and vapor flow rate. A performance indicator may then be determined (at step 173 of FIG. 9) using a performance model 112 of the power plant controller 22. The performance indicator may provide an operational characteristic, such as efficiency, of the power plant 12. The performance model 112 may be configured to use thermodynamic and / or physical details of the power plant 12 as well as the setpoint values determined by the control model 111 to determine a value of an operating characteristic of the power plant 112. The performance model 112 may be configured to take into account additional information, such as environmental conditions, market conditions, process conditions, and / or other relevant information.
In addition, according to certain aspects of the present invention, an estimate of life cycle cost (LCC) of the power plant 12 may be determined (at step 174 of FIG. 9), such as with an LCC model 113 used in the power plant controller 22 of FIG 8 is included. The LCC model 113, which may be a computer program or the like, may be configured to use physical and / or cost information about the power plant 12 as well as setpoints from the control model 111 to determine estimated life cycle costs of the power plant 12. The life cycle costs may include, for example, total costs, maintenance costs and / or operating costs of the power plant 12 during its lifetime. The LCC model 113 may additionally be configured to account for the results of the performance model 112 for improved precision. The LCC model 113 may therefore use the particular setpoint values of the control model 111 and the performance features of the performance model 112 as well as other information as desired to estimate the life of the power plant 12, as well as how much it may cost the power plant 12 during its lifetime operate and / or maintain. As mentioned above, the life of a power plant can be expressed in terms of hours of operation and / or number of starts, and a given power plant has an expected life that can be provided by a manufacturer of the power plant. Predefined values of expected life can therefore be used at least as a starting point for the LCC model 113 and / or an enhancement module 114.
Using information from other embodiments of the invention, such as results from determining an initial setpoint, a performance indicator, and an estimated life cycle, an optimization problem for the power plant 12 may be resolved (at step 175) as described below. Such an optimization problem may have multiple equations and variables that depend on the desired depth of analysis, and may have an objective function, which in embodiments may be an objective function based on LCC. The solution may include providing an improved or enhanced operating parameter of the power plant 12, such as by minimizing an LCC-based goal setting function (also step 175). In embodiments, the solution to the optimization problem may be performed by an enhancement module 114 of the power plant controller 22 of FIG. 8.
From optimization theory, it is known that a goal setting function represents a feature or parameter that is to be optimized, and can accommodate many variables and / or parameters depending on how the optimization problem is defined. In an optimization problem, an objective setting function may be maximized or minimized depending on the particular problem and / or parameter represented by the objective setting function. For example, as stated above, an objective setting function that expresses LCC according to embodiments would be minimized to generate at least one operating parameter that is used to operate the power plant 12 such that the LCCs are kept as low as feasible. An optimization problem for the power plant 12 or at least one objective setting function may include factors such as power plant characteristics, location parameters, customer specifications, results from the control model 111, performance model 112 and / or LCC model 113, environmental conditions, market conditions and / or process conditions, and any additional information are suitable and / or desired, take into account. Such factors may be collected in terms of a goal setting function, such that an LCC based goal setting function may include, for example, maintenance costs and operating costs over time, where time is a prediction horizon based on an estimated component life. It should be understood that complex goal setting functions and / or optimization problems can be used in implementations of the present invention, as they may each have many or all of the different functions and / or factors described herein.
Maintenance costs may be determined, for example, by modeling portions of the power plant 12 to estimate wear based on various parameters, such as those already discussed. It is understood that any part of the power plant 12 may be modeled for these purposes. However, in a practical application, parts connected to fewer, larger, or fewer selected portions of the power plant 12 could be modeled, and / or constants or plug values could be used for some parts instead of modeling. Regardless of the level of detail that is used, minimization of such an LCC-based goal setting function is part of an optimization problem that may vary for a given power plant due to many factors, such as those provided above, and at least one improved or enhanced power station operating parameter 12, such as in accordance with minimizing the LCC. In addition, those skilled in the art will recognize that at least one optimization constraint may impose at least one constraint, such as a predetermined operating time and / or down time, a predefined upper and / or lower temperature at various locations in the power plant 12, a predetermined torque, a predetermined power output and / or other requirements as desired and / or aptitude. Unless otherwise stated, it is within the purview of those skilled in the art to determine what constraints should be applied to a given optimization problem and how. Those skilled in the art will further recognize situations in which additional optimization theory techniques may be employed, such as adding a slip variable. to allow a workable solution to the optimization problem.
Known techniques may be used, such as the enhancement module 114 (FIG. 8), to solve an optimization problem for the operation of the power plant 12. Integer programming, linear, mixed-integer linear, mixed-integer non-linear and / or other techniques may be used as appropriate and / or desired. Additionally, as seen in the exemplary goal setting function, the optimization problem may be solved over a prediction horizon that provides a series of values for at least one operating parameter of the power plant 12. While the enhancement or enhancement may be performed over a relatively short prediction horizon, such as 24 hours, or even on the order of minutes, depending on a desired depth of analysis, the enhancement module 114 (Figure 8) may use a longer prediction horizon, such as Example up to an estimated lifetime of the power plant 12. In embodiments, initial setpoints may be determined, such as by the control model 111 (FIG. 8), in response to and / or as part of the solution to the optimization problem, to improve or enhance to give optimized setpoint. Additionally, iteration may be used in determining an initial setpoint, determining a value of a performance indicator, determining estimated LCC, and improving or increasing (at steps 172-175 of FIG. 9) to refine results and / or improve control setpoints of power plant 12 increase.
As will be described, a bid curve section 180 may generate a bid curve or a set of bid curves, an example of which was previously shown in connection with FIG. In the power plant controller 22, control information 115 may be received from a supply curve module 120 from the control module 110 and / or data sources 26 (at step 181 of FIG. 9). According to certain embodiments, the control information 115 includes: control setpoints, power, environmental conditions, and / or market conditions. This information may also be known as "as-run" information. In addition, an environmental condition forecast 121 and / or market condition forecasts 122 may be received (at step 182). According to certain embodiments, a database 123 may be included and store current information, "as-run" information, and / or historical information locally, including any or all environmental conditions, market conditions, power plant performance information, offer curves, control setpoints, and / or any other information that may be appropriate could be. The database 123 may be used to provide information to simulate the operation of the power plant 12 (at step 183), such as with an off-line model 124 of the power plant 12.
The offline model 124 may have a similar model to the control model 111, but may also include additional modeling information. The offline model 124 may include, for example, sections or the complete control model 111, performance model 112, LCC model 113, and / or additional modeling information. By executing the offline model 124 with setpoints and / or information from improving or increasing the LCC, output from the offline model 124 may be used to estimate the cost of power production for each time interval in a forecast horizon and for different power output values of the power plant 12 to generate one or more supply curves 125 (at step 184) that are sent to the dispatching authority 24 (at step 185) or otherwise provided. The offline model 124 may use any suitable information, such as historical, current, and / or predicted information, to determine estimated operating costs and / or conditions of the power plant 12. Additionally, in embodiments (at step 186), the offline model 124 may be tuned by a model tuning module 126, for example. The tuning may include, for example, periodically adjusting parameters for the offline model 124 based on information received and / or provided by other portions of the power plant controller 22 to better reflect the current operation of the power plant 12, the operation of the power plant 12 better to simulate. For a given set of operating parameters, if the power plant controller 12 observes a current process condition other than what the offline model 124 had predicted, the off-line model 124 may change accordingly.
In addition to the supply curves 125 from the power plant 12a, as illustrated, the dispatching authority 24 may receive supply curves 125 from other power plants 12b that it controls. The dispatching authority 24 may judge the supply curves 125 and generate allocation scheduling to adjust load on the power system 10. The dispatching authority 24 may additionally consider predicted environmental conditions, load forecasting, and / or other information as appropriate and / or desire that it may receive from different local or remote data sources 26 to which it has access. As illustrated, the scheduling generated by the dispatching authority 24 includes a control signal for the power plant 12 having a target load 128 to which the power plant controller 22 may respond as described above.
It will be understood that including the life cycle cost considerations, as described herein, may serve to increase the scope and precision of the power plant models used in the optimization process, thereby enabling improvements to the approach. Supply curves 125, as described above, can represent variable costs (measured in dollars per megawatt hour compared to power plant output in megawatts). The supply curves 105 and 20 may include an incremental variable cost supply curve and a medium variable cost supply curve. As can be seen, embodiments of the present invention may provide precise assessments of variable costs over their generated supply curves 125. Using embodiments of the present invention, it has been found that incremental variable cost supply curves predict current incremental variable cost curves very closely, while average variable cost supply curves have been found to predict actual average variable cost curves very closely. The precision of the offer curves generated by embodiments of the present invention indicates that the various models used in the power plant controller 22 of FIG. 8 provide a reasonably representative model for the purposes set forth.
Other aspects of the present invention will be described with reference to and including certain systems and methods provided above with reference to Figs. 10-12. 10 is a dataflow diagram that illustrates an architecture for a power plant optimization system 200 that may be used in a combined cycle power plant having gas and steam turbine systems. In the provided embodiment, a system 200 includes monitoring and control instruments 202, 204, such as sensors and actuators discussed above, each combined with the gas turbine system 202 and the steam turbine system 204. Each of the monitoring and control instruments 202, 204 may transmit signals indicative of measured operating parameters to a power plant controller 208. The power plant controller 208 receives the signals, processes the signals in accordance with predetermined algorithms, and transmits control signals to the monitoring and control instruments 202, 204 to affect changes in power plant operations.
The power plant controller 208 interfaces with a data acquisition module 210. The data acquisition module 210 may be coupled in conjunction with a database / historian 212, which maintains archive data for future reference and analysis. A heat balance module 214 may receive data from the data acquisition model 210 and the database / historian 212 as required to process algorithms that tune a power plant's mass and energy balance model to match measured data as closely as possible. Discrepancies between the model and the measured ones can indicate errors in the data. It will be understood that a power module 216 uses power plant equipment models to predict the expected performance of the power plant major components and equipment. The difference between expected and current performance may represent deterioration in the state of power plant equipment, parts, and components, such as, but not limited to, contamination, calcification corrosion, and breakage. In accordance with aspects of the present invention, the power module 216 may track degradation over time so that power issues that have the most significant effect on power plant performance are identified.
As illustrated, an optimizer module 218 may be included. The optimizer module 218 may include a methodology for optimizing an economic allocation of the power plant. For example, according to embodiments, the power plant may be allocated based on the heat input coefficient or the incremental heat input coefficient, based on the assumption that the heat input coefficient is equal to financial resources. In an alternative scenario, where the power plant has an additional manufacturing process (not shown) where steam is used directly (that is, where steam generated is diverted from power generation in the steam turbine to another manufacturing purpose), it is understood that the optimizer module 218 may solve an optimization problem whereby a component having a higher heat input coefficient may be allocated. For example, in certain situations a demand for steam may exceed an electricity demand, or the electricity output may be constrained by requirements of the electrical system. In such cases, allocating a lower efficiency gas turbine may allow for greater heat to be gained without increasing the current output beyond a limit. In such scenarios, allocating the component with a higher heat input coefficient is the economically optimized alternative.
The optimizer module 218 may be selectable between an online (automatic) and an offline (manual) mode. In the online mode, the optimizer 218 automatically calculates current power plant economics parameters, such as cost of electricity generated, incremental cost at each generation level, cost. the process steam and power plant operating profit at a predetermined periodicity, for example in real time or once every five minutes. An offline mode can be used to simulate constant performance, analyze "what if" scenarios, analyze budget and upgrade options, and analyze current power generation capacity, target heat cost coefficient, current power plant operation correction, conditions, impact of operational constraints, and Predict maintenance actions and fuel consumption. The optimizer 218 calculates a profit-optimized output for the power plant based on economic real-time cost data, issue prices, load levels and equipment degradation rather than output based on efficiency by combining power plant heat balances with a financial model of the power plant. The optimizer 218 may be tuned to tune the degradation of each component individually and may generate an advisory output 220 and / or may generate a closed feedback loop control output 222. The Advisory Issue 220 recommends operators to adjust controllable parameters of the power plant to optimize each power plant component to maximize profitability. In the exemplary embodiment, advisory output 220 is a computer display screen that is communicatively coupled to a computer executing optimizer module 218. In an alternative embodiment, the advisory output is a display screen of a remote workstation, the workstation accessing the optimizer module 218 through a network. The closed feedback loop control output 222 may receive data from the optimizer module 218 and calculate optimized setpoints and / or feedback settings for the modules of the system 200 to implement real time feedback control.
11 is a simplified block diagram of a real-time thermal power plant optimization system 230 that, in accordance with aspects of the present invention, includes a server system 231 and multiple client subsystems, also referred to as client systems 234, that are communicatively coupled to the server system 231 are. As used herein, real-time refers to results that occur in a substantially short period of time after the inputs that affect the result, such as computer calculations. The time span represents the amount of time between two iterations of a regularly repeated task. Such repeated tasks may here be called periodic tasks or cycles. The time period is a design parameter of the real-time system that can be selected based on the importance of the result and / or the ability of the system that implements the processing of the inputs to produce the result. In addition, events that occur in real time occur without significant intentional delay. In the exemplary embodiment, calculations may be updated in real time with a periodicity of one minute or less. Client systems 234 may be computers having a web browser such that the server system 231 is accessible to client systems 234 via the Internet or other network. Client systems 234 may be interconnected with the Internet through many interfaces. Client systems 234 could be any device capable of interconnecting to the Internet. A database server 236 is connected to a database 239 containing information associated with a plurality of topics as described in greater detail below. In one embodiment, a centralized database 239 having aspects of data sources 26 discussed above is stored on a server system 231 and is for potential users on one of the client systems 234 by logging on to the server system 231 by the client systems 234 accessible. In an alternative embodiment, the database 239 is stored remotely from the server system 231 and can not be centralized.
In accordance with aspects of the present invention, certain of the control methods discussed above may be developed for use in conjunction with system diagrams of FIGS. 10 and 11. For example, one method includes simulating power plant performance using a power plant power module of a software code segment that receives power plant monitoring instrument data. The data may be received by a network from a power plant controller or a database / historian software program running on a server. Any additional power plant components, such as an intake conditioning system or HRSG pipe firing system, may be simulated in a similar manner to that used to simulate power plant performance. Determining the power of each power plant component in the same manner allows the entire power plant to be treated as a single power plant to determine optimized setpoint values for the power plant, rather than separately determining such setpoints for each component. Measurable quantities for each power plant component can be parameterized to express output or power plant efficiency on a component-by-component basis. The parameterization of power plant equipment and power plant performance includes computing efficiency for components such as, but not limited to, a gas turbine compressor, a gas turbine, a heat recovery steam generator (HRSG), a suction fan, a cooling tower, a condenser, a Feed water heaters, an evaporator, a flash tank, etc. Similarly, it will be understood that the heat cost coefficient and power calculations can be parameterized and the resulting simultaneous equations solved in real time, so that the calculated results will be calculated without intentional delay from the time in which each parameter was sampled are available. Solving parameterized concurrent equations and constraints may also include determining a current heat balance for the power plant, determining an expected power using existing constraints on the operation of the power plant, such as, but not limited to, control reserve requirements, power system demand, maintenance activities , Fresh water demand and component failures. Solving parameterized equations and constraints may also include determining parameters to set or change the current heat balance such that a future heat balance equals the determined expected performance. As an alternative embodiment, solving parametrized simultaneous equations and constraints includes determining inlet conditions into the plant, predicting an output of the plant based on the determined inlet conditions, and a predetermined model of the plant determining a current output of the plant, comparing the predicted ones Output with the particular output and setting the power plant parameters until the particular output equals the predicted output. In exemplary embodiments, the method also includes correlating controllable power plant parameters, power plant equipment, and power plant performance using parameterized equations, defining the objective of the optimization using an objective setting function that minimizes the thermal effort coefficient of the power plant, and / or maximizing the power plant's and Defining the physically possible operating range of each piece of equipment and / or total limits using constraints, the total limits having maximum power production, maximum fuel consumption, etc.
FIG. 12 is a flow chart of an example method 250 for solving parameterized concurrent equations and constraints according to the present invention. The method 250 includes determining (at 252) a current heat balance for the power plant, determining (at 254) an expected power using current constraints for operation, and determining (at 256) tuning parameters by the current one To change the heat balance such that a future heat balance is equal to the particular expected performance. The method 250 also includes determining 258 intake conditions in the power plant, predicting 260 an output of the power plant based on the determined intake conditions and a predetermined model of the power plant, determining 262 a current output of the power plant, comparing the predicted output with the certain output and setting 266 of power plant parameters until the particular output is equal to the predicted output. It should be understood that the described method and systems, discussed in connection with Figs. 10 and 11, provide a cost effective and reliable means for optimizing combined cycle power plants.
Attention is drawn to the several flowcharts and system configurations that illustrate the control methodology in accordance with certain aspects of the present invention, with reference to FIGS. 13-16. In general, according to an example embodiment, a control system for a heat generation unit, such as the gas turbine system, or a power plant may include first and second instances of a model modeling the operation of the turbine, such as by using models based on physics or mathematical modeling (for example, transfer functions, etc.). The first model (which may also be called the "primary model") may provide existing operating parameters of the gas turbine system describing the operating mode of the turbines and operating conditions that correspond to it. As used herein, "parameter" refers to elements that may be used to define the operating conditions of the turbine, such as, but not limited to, temperatures, pressures, gas flows at particular locations in the turbine, and compressor Performance parameters may also be called "model correction factors" which refer to factors used to set the first or second model to reflect the operation of the turbine. Inputs to the first model may be detected or measured and provided by one operable. In addition to current performance parameters, the method of the present invention may include receiving or otherwise obtaining information about external factors or disturbance variables, such as environmental conditions, that may affect the current or future operation of the gas turbine system.
The second model (also referred to herein as a "secondary model" or a "predictive model") is generated to identify or predict one or more operating parameters, such as controlled variables, of the gas turbine system, taking into account the present operating parameters, such as for example, manipulated variables, and the one or more confounding variables. Exemplary operating parameters of the turbine include, but are not limited to, actual turbine operating conditions such as exhaust gas temperature, turbine output, compressor pressure ratios, heat input coefficient, emissions, fuel consumption, expected revenues, and the like. This second or predictive model may therefore be used to indicate or predict turbine behavior at particular operating setpoints, performance objectives, or operating conditions that are different than the present operating conditions. As used herein, the term "model" generally refers to modeling, simulating, predicting, or indicating based on the output of the model. It will be understood that while the term "second model" is used herein, in some cases there is no difference between the formulation of the first and second models, so that the "second model" is the execution of the first model with set parameters or additional or different input.
By modeling the turbine operating behavior using the second or predictive model taking into account external factors and / or different operating conditions, the turbine control can thus be adjusted to operate more efficiently under these different operating conditions or in view of the unexpected external factors. This system therefore allows automated turbine control based on modeled behavior and operating characteristics. In addition, the described modeling system allows for the creation of operator specified scenarios, inputs, operating points, operational objectives, and / or operating conditions to predict turbine behavior and operating characteristics under these operator specified conditions. Predicting such hypothetical scenarios allows operators to make better-informed tax and operational decisions, such as planning, loading, offsetting, etc. As used herein, the term "operating points" generally refers to operating points, conditions and / or objectives and is not intended to to be limiting. An operating point may therefore relate to objectives or setpoint, such as base load, deceleration point, peak fire, and the like.
An exemplary use of the described turbine modeling system includes adjusting the turbine operation to meet grid compatibility requirements while still operating at the most efficient levels. Regional network supervisors typically dictate that power plants must be able to support a network during frequency disturbances. Supporting the network during disturbances involves increasing or decreasing the turbine load under certain conditions, depending on the state of the network. For example, during a fault, a power plant is expected to increase its power generation output (eg, up to 2%) to compensate for other utility outages. Turbine operation therefore typically limits the base load point to allow the turbine to operate at a covered output level (also called the "reserve margin") so that the increased load can be provided as needed without the additional maintenance factor associated with Overfiring is incurred. As an example, the reserve margin may be 98% of what the base load would typically be, so allowing the load to be increased to accommodate network requirements (eg, increase by 2%) without exceeding the base load of 100%. However, unexpected external factors such as temperature, humidity, or pressure can negatively impact turbine efficiency. As the heat of the day increases, a turbine can not have that 2% reserve it needs because heat has caused the turbine to operate less efficiently, and the turbine can not reach that 100% load as originally planned. For compensation, conventional heat load coefficients curves cause the turbine to operate in a more efficient condition throughout the day given the possible machine efficiency loss (for example, at 96%, etc.).However, the turbine modeling system described herein allows modeling of turbine behavior in real time according to current external factors (eg, temperature, humidity, pressure, etc.) and therefore controlling turbine operation to operate efficiently in the current environmental conditions. Similarly, future turbine behavior may be predicted to predict turbine behavior in response to a day's thermal fluctuations, allowing turbine plant design to achieve the most efficient and economically viable operation. As another example, power plants typically make decisions related to shutting down gas turbines during the night or simply reducing the output level (eg, aborting). Turbine operating characteristics, such as emissions, exhaust gas temperature and the like, affect this decision. By using the turbine modeling system described herein, decisions can be made on a smarter basis, either before or in real time, or near real time. External factors and expected turbine operating parameters may be provided to the second model to determine what the turbine operating parameters would be. The modeled features can therefore be used to determine whether a turbine should be shut down or stalled in light of these characteristics (eg, efficiency, emissions, costs, etc.).
As yet another example, a turbine modeling system may be used to assess the benefits of performing turbine maintenance at a given time. The turbine modeling system of the present invention may be used to model the operating characteristics of the turbine at its current capabilities based on current performance parameters. Then, an operator-specified scenario may be created that models the operating characteristics of the turbine if maintenance is performed (for example, improving the performance parameter values to show an expected performance boost). As turbines worsen over time, the performance parameters reflect machine degradation. In some cases, maintenance may be performed to improve these performance parameters, and therefore the operating characteristics of the turbine. By modeling or predicting the improved operational characteristics, a cost-benefit analysis can be performed to compare the benefits that can be achieved by performing the maintenance with the costs incurred.
FIG. 13 illustrates an exemplary system 300 that may be used to model turbine performance. According to this embodiment, there is provided a power plant 302 having a gas turbine having a compressor and a combustion chamber. An inlet line to the compressor supplies ambient air and possibly water injected into the compressor. The configuration of the inlet line contributes to a pressure loss of ambient air flowing into the compressor. An exhaust conduit for the power plant 302 routes combustion gases from the outlet of the power plant 302, for example, through emission control and sound absorbing devices. The amount of inlet pressure loss and backpressure may vary over time due to the addition of components to the inlet and exhaust conduits and due to plugging of the inlet and exhaust conduits.
The operation of the power plant 302 may be monitored by one or more sensors sensing one or more observable conditions or operating or performance parameters of the power plant 302. In addition, external factors such as the environment of one or more sensors may be measured. In many cases, two or three redundant sensors can measure one and the same parameter. For example, groups of redundant temperature sensors may monitor ambient air surrounding power plant 302, compressor outlet temperature, turbine exhaust temperature, and other temperatures in power plant 302. Similarly, groups of redundant pressure sensors may monitor ambient pressure and static and dynamic pressure levels at the compressor inlet and outlet, turbine exhaust, and other locations in the engine. Groups of redundant moisture sensors can measure ambient humidity in the inlet line of the compressor. Groups of redundant sensors may also include flow sensors, velocity sensors, flame sensor sensors, valve position sensors, vane angle sensors, or the like that sense different parameters relevant to the operation of the power plant 302. A fuel control system may regulate the fuel flowing from a fuel supply to the combustion chamber. The fuel control device may also select the fuel type for the combustion chamber.
As mentioned, "operating parameters" refers to elements that may be used to define the operating conditions of the turbine system, such as temperatures, pressures, compressor pressure ratio, gas flows at defined locations in the turbine, load setpoint, firing temperature, and one or more Conditions that correspond to the level of deterioration of the turbine or compressor and / or the level of turbine or compressor efficiency. Such parameters are measured directly. Other parameters are estimated by the turbine models or are indirectly known. Still other parameters may represent hypothetical or future conditions and may be defined by the power plant operator. The measured and estimated parameters may be used to represent given turbine operating conditions. As used herein, "performance indicators" are operating parameters that are derived from values of certain measured operating parameters and are a performance criterion for operation of the power plant during a defined period of time. Performance indicators include, for example, the heat input coefficient, the output level, and so on.
As illustrated in Figure 13, the system 300 includes one or more controllers 303a, 303b, each of which may be a computer system having one or more processors executing programs to control the operation of a power plant or generating unit 302 to control. Although FIG. 13 illustrates two controllers, it will be understood that a single controller 303 may be provided. According to a preferred embodiment, a plurality of control devices may be included to provide redundant and / or distributed processing. For example, the control actions may depend on sensor inputs or instructions from power plant operators. The programs executed by the controller 303 may include scheduling algorithms such as those for regulating fuel flow to the combustor, managing network compatibility, adjusting, etc. The commands generated by the controller 303 may cause actuators on the turbine, for example, valves between the fuel supply and adjust the combustors to regulate the fuel flow, fuel distributions, and fuel type. Actuators can adjust inlet guide vanes on the compressor or activate other control setpoints on the turbine. It is understood that the controller 303 may be used to generate the first and / or second models as described herein, in addition to facilitating control of the power plant. The controller 303 may receive operator and / or present modeled output (or any other system output). As previously described, the controller 303 may include a memory that stores programmed logic (eg, software) and may store data such as sensed operating parameters, modeled operating parameters, operating limits and targets, operating profiles, and the like. A processor may use the operating system to execute the programmed logic and may also use data stored thereon. The users may turn on to the controller 303 via at least one user interface device. The controller 303 may communicate with the power plant online while it is operating, as well as communicate with the power plant offline while it is not operating via an I / O interface. It is understood that one or more of the controllers 303 may perform the embodiment of the model-based control system described herein, including but not limited to: acquiring, modeling, and / or receiving operating parameters and performance parameters; Generating a first power plant model representative of the current turbine operation; Detecting, modeling and / or receiving information from external factors; Receiving operator input, such as performance goals, and other variables; Generating a second power plant model that reflects the operation in view of the additional data provided; Controlling present or future turbine operation and / or presenting modeled operational characteristics. In addition, it is understood that other external devices or multiple other power plants or generating units may communicate with the controller 303 via I / O interfaces. The controller 303 may be remote with respect to the power plant that controls it. Further, the controller 303 and the programmed logic implemented thereby may include software, hardware, firmware, or any combination thereof.
The first controller 303a (which, as mentioned, may be the same or a different controller than the controller 303b), may be operable to model the plant 302 by a first or primary model 305 that models the current performance parameters of the engine The second controller 303b may be operated to model turbine operating characteristics under different conditions based on a second or predictive model 306. The first model 305 and the second model 306 may each be an assembly of one or more turbine behavior mathematical representations Representations may rely on input values to generate an estimate of a modeled operating parameter In some circumstances, the mathematical representations may generate a substitute operating parameter value that may be used under circumstances where the aggregate ssene parameter value is not available. The first model 305 may then be used to provide a basis and / or input to the second model 306, turbine operating characteristics based on the current performance parameters of the power plant 302, and any other factors, such as external factors, commands provided by the operator or conditions and / or set operating conditions. As described above, it will be understood that "the second model 306" may simply be an instance of the same model as the first model 305, taking into account additional or different inputs, such as external factors, different operating points, different performance parameters, or different turbine behavior to model the different inputs. The system 301 may further include an interface 307.
With further reference to Figure 13, a brief description of the interrelationships between the system components is provided. As described, the first or primary model 305 models current performance parameters 308 of the power plant 302. These actual performance parameters 308 may include, but are not limited to, conditions that correspond to the level of turbine degradation, conditions that correspond to the level of turbine efficiency (eg heat load coefficient or fuel / power output ratio), inlet guide vane angle, fuel flow rate, turbine speed, compressor inlet pressure and temperature, compressor outlet pressure and temperature, turbine exhaust temperature, generator power output, compressor airflow, combustor fuel / air ratio, firing temperature (turbine inlet), combustor flame temperature, fuel system pressure ratios, and acoustic characteristics. Some of these performance parameters 308 may be measured or detected directly from the turbine operation, and some may be modeled based on other measured or sensed parameters. The performance parameters may be provided by the first model 305 and / or may be generally provided by the controller, such as when sensed and / or measured by the controller. In generating the first model 305, the performance parameters 308 (which are intended to refer to any turbine behavior provided by the model) are provided for generating the second or predictive model 306. Other variables 309 may be provided to the second model 306 depending on its intended use. The other variables may include, for example, external factors, such as environmental conditions, which are generally uncontrollable and must be taken into account. In addition, the other variables 309 may include a scenario specified by the controller or an operating point (eg, a turbine operating point generated or otherwise provided via the controller 303, such as turbine control based on the first model 305, etc.), measured inputs, which may be some or all of the same measured inputs as described as they may be modeled by the first model 305. As described with reference to FIG. 14 below, an operator specified scenario 313 (eg, one or more operator supplied commands indicating different turbine operating points or conditions) may also be provided to the second model 306 via operator input. As an example use, the other variables 309 may include, for example, a scenario specified by the controller that may be provided as one or more inputs to the second model 306 when attempting real-time or near real-time actual turbine behavior based on additional inputs , such as external factors or measured inputs. By using a scenario of the first model specified by the controller in addition to one or more of these additional inputs, the expected real-time behavior of the power plant 302 may be modeled by the second model 306 taking into account these additional inputs, which in turn may be used to calculate the Control power plant 302 or adjust the first model 305 by control profile inputs 310.
Referring to FIG. 14, an operator specified operating mode or scenario 313 is input as one or more inputs via interface 307 to the second or predictive model 306, which then models or predicts future turbine behavior under a variety of conditions. For example, an operator may provide commands to the interface 307 to create a scenario in which the power plant 302 operates at a different operating point (eg, different loads, configuration, efficiency, etc.). As an illustrative example, a set of operating conditions may be provided via the operator-specified scenario 313, which represents conditions expected for the following day (or any future time frame), such as environmental conditions or demand requests. These conditions may then be used by the second model 306 to generate expected or predicted turbine operating characteristics 314 for the power plant 302 during this time frame. When executing the second model 306 under the operator specified scenario, the predicted operating characteristics 314 include, but are not limited to, turbine behaviors such as base load output capability, peak output capability, minimum trim points, emission levels, thermal effort coefficient, and the like. These modeled or predicted operational features 313 may be useful in planning and committing to power generation levels, such as for day-ahead market planning and offers.
Fig. 15 illustrates an example method 320 with which an embodiment of the invention may operate. A flowchart of the basic operation of a system for modeling a turbine as provided by one or more control devices, such as those described with reference to FIGS. 13 and 14, is provided. The method 320 may begin at step 325 where the controller may model, by a first or primary model, one or more current performance parameters of a turbine according to the current operation. To generate this first model, the controller may receive as inputs to the model one or more operating parameters indicative of the current operation of the turbine. As described above, these operating parameters may be detected or measured and / or they may be modeled as may occur if the parameters can not be detected. The current operating parameters may include any parameter indicative of current turbine operation as described above. It will be understood that the methods and systems disclosed herein do not directly depend on whether the operating parameters are measured or modeled. The control device may, for example, comprise a generated model of the gas turbine. The model may be an arrangement of one or more mathematical representations of the operating parameters. Each of these representations may use input values to produce an estimate of a modeled operating parameter. The mathematical representations may generate a substitute operational parameter value that may be used under circumstances in which a measured parameter value is not available.
At step 330, the controller may receive or otherwise determine one or more external factors that may affect current and / or future operation. As described above, these external factors are typically (but not necessarily) uncontrollable, and the incorporation of their influence into the second model is beneficial to produce the desired turbine control profile and / or performance. External factors may include, but are not limited to, ambient temperature, humidity or air pressure, and fuel composition and / or supply pressure, which may affect turbine performance. These external factors may be measured or detected, estimated or otherwise manually provided by an operator (such as the operator requesting predicted behavior based on hypothetical scenarios or future conditions), and / or may be provided by third-party information sources (e.g., weather services etc.).
At step 335, the controller may receive set operating points and / or other variables to predict turbine behavior at a condition that is different than the current turbine condition. Set operating points may include, but are not limited to, identifying the desired level of output, such as modeling the turbine at a reserve margin (eg, 98% of the base load) or, for example, modeling the turbine at a peak load or during trim. Operating points may also include operating limits such as, but not limited to, fuel gas path durability (or firing temperature), exhaust gas frame durability, NOx emissions, CO emissions, lean burn chamber quench, combustion dynamics, compressor pumps, compressor journeys, aeromechanical compressor limits, Compressor plays and compressor outlet temperature. Thus, by providing these adjusted operating points or other variables, the operator may provide hypothetical scenarios for which the turbine model predicts the operating characteristics under these scenarios, which may be useful for controlling future operation of the turbine and / or planning future power generation and obligations.
Step 335 is followed by step 340 where a second or predictive model of the turbine is generated based on the first model generated at step 325, and optionally based on the external factors and / or set operating points, or others variables provided at step 335. This second or predictive model may therefore accurately specify or predict operating parameters and, based thereon, performance indicators for the turbine during a future period of operation.
At step 345, the modeled power may be used to adjust current or future turbine operation and / or to display the modeled power to an operator. Thus, in adjusting current turbine operation, the turbine controller may receive the modeled performance parameters as inputs to change a current control model (eg, the first model) or a current control profile, such as by changing different setpoints and / or references used for current turbine control become. It is expected that this real-time or near-real-time control of the turbine would be performed if the inputs to the second model generated at step 340 are representative of the current turbine conditions or current external factors. For example, real-time or near-real-time adjustment at step 345 may be performed when the second model represents performance characteristics taking into account the current temperature, pressure, or humidity and / or taking into account turbine operating parameters or performance parameters, turbine degradation, and / or more accurately represent efficiency. FIG. 16 describes an exemplary embodiment that may optionally receive specific operator inputs and generate predicted behavior under a different operating condition. The output of the model generated at step 340 may also be displayed to an operator via an interface or otherwise presented. In one embodiment, where the operator provides hypothetical operating scenarios at step 335, the predicted turbine operating characteristics may be displayed, for example, for analysis and eventual inclusion in future control or planning activities. The method 320 may therefore end after step 345, after modeling the current performance parameters of the turbine through a first model, and then modeling the same turbine in consideration of the additional external factors, setting operating points, or other additional data to turbine operation based thereon predict additional data.
FIG. 16 illustrates an example method 400 with which an embodiment of the invention may operate. A flow chart of the operation of a system for modeling a turbine as provided by one or more control devices, such as those described with reference to FIGS. 13 and 14, is provided. The method 400 illustrates the use of the system 301, where an operator may optionally provide additional variables to use the modeling capabilities to predict turbine behavior under hypothetical scenarios. The method 400 may begin at decision step 405, where it is determined whether the turbine is to be modeled according to current turbine operating parameters and performance parameters, or whether operator-provided parameters are to be considered in generating the model. For example, if the system is used to predict hypothetical operating scenarios, current performance parameters may not be required as inputs to the model (assuming that the model already reflects basic turbine operation and turbine behavior). Thus, if it is determined at decision step 405 that current parameters are not to be used, operations proceed to step 410 where the operator provides different performance parameters that allow the turbine to be modeled under a different operating point and under a different operating condition (e.g. Example, in a more deteriorated state, at a different efficiency level, etc.). Otherwise, current performance parameters and / or operating parameters are used, as described with reference to step 325 of FIG. 15, and operations proceed to step 415. At step 415, the controller may model by a first or primary model one or more performance parameters of a turbine according to either the operator supplied input of step 410 or the current turbine operation. For example, if the model is generated based at least in part on parameters provided by the operator at step 410, the predicted turbine behavior model generated at step 415 is representative of these performance parameters.
Step 415 is followed by decision step 420, which determines whether subsequent modeling (eg, the "second model" or the "predictive model") is to be based on current external factors, such as current temperature, current pressure or actual humidity, or on different external factors provided by the operator. For example, in one scenario, the controller may model turbine operating behavior based on the additional data of one or more current external factors that would allow further prediction of turbine behavior in light of current conditions. However, in another scenario, the controller may be used to further model the turbine according to the operator supplied conditions, allowing for predicting turbine operating characteristics under different hypothetical scenarios. Thus, if it is determined in step 320 that the operator-supplied external factor data is to be considered in modeling, then the operations continue to step 425. Otherwise, the operations continue to step 430 using current external factors. At step 430, the controller receives external factors to be considered in generating the second or predictive model, whether they are representative of the current state or hypothetical factors. Subsequent to step 430, steps 435-445 optionally follow the consideration of different operating points, generate the predictive model based on the received data, and display the predicted behavior in the same or similar manner as with reference to steps 325-345 of FIG Figure described, allow. The method 400 may end after step 445 after turbine operation behavior is optionally modeled based on operator specified scenarios.
Embodiments described herein thus allow using turbine models to indicate turbine behavior and corresponding operating parameters of an actual turbine in addition to predicting turbine behavior taking into account the actual performance parameters and one or more identified external factors. These embodiments therefore provide a technical effect of indicating or predicting turbine behavior at operating points or operating conditions that are different than current turbine operation. Yet another additional technical effect is provided that allows automated turbine control based at least in part on modeled behaviors and operating features, which may optionally include application of operator-specified scenarios, inputs, operating points, and / or operating conditions to determine turbine behavior and operating characteristics at those operator-specified Predict conditions. Another realized technical effect has the ability to predict different hypothetical scenarios, allowing operators to make more informed control and operational decisions, such as scheduling, weighting, offsetting, etc. It should be understood that references herein to step diagrams of Systems, methods, devices and computer program products according to exemplary embodiments of the invention.
Referring to Figure 17, a flow diagram 500 is illustrated in accordance with an alternative embodiment of the present invention. It is understood that the flowchart 500 has aspects that may be used as a control method or as part of a control system to facilitate the optimization of a power plant 501. Power plant 501 may be similar to any of those discussed with respect to FIGS. 2 and 3, although, unless otherwise limited in the appended claims, it is understood that the present invention also applies to other types of power plants can be. In a preferred embodiment, the power plant 501 may include a plurality of heat generating units that generate electricity sold within a power system market such as that discussed in relation to FIG. 1. The power plant 501 may have many possible types of operating modes, including, for example, the different ways in which heat generating units of the power plant are required or operated, the output levels of the power plant, the ways in which the power plant responds to changing environmental conditions It should be understood that the operating modes may be described and defined by operating parameters pertaining to physical characteristics of particular aspects of the operation of the power plant 501. As further illustrated in FIG. 17, the present invention may include a power plant model 502. The power plant model 502 may include a computerized representation of the power plant that correlates process inputs and process outputs as part of a simulation intended to mimic the operation of the power plant. As shown, the present invention further includes a tuning module 503, a power plant controller 505, a tuned power plant model 507, a power plant operator module 509, and an optimizer 510, discussed below in detail.
The power plant 501 may include sensors 511 that measure operating parameters. These sensors 511 as well as the operating parameters they measure may have any of those already discussed herein. As part of the present method, the sensors 511 may receive measurements of operating parameters during an initial, current, or first operating period (hereafter referred to as "first operating period"), and these measurements may be used to tune a mathematical model of the power plant, then as below may be used as part of an optimization process to control power plant 501 in an enhanced or optimized mode during a subsequent or second operating period (hereafter referred to as "second operating period"). The measured operating parameters themselves may be used to estimate power plant performance or may be used in calculations to derive performance indicators that reflect specific aspects of the operation and performance of the power plant. It is understood that performance indicators of this type can have the heat cost coefficient, the efficiency, the generation capacity, and others. As an initial step, therefore, operating parameters measured by the sensors 511 during the first operating period may be used as one or more performance indicators (or used to calculate values for them). As used herein, such values for performance indicators (that is, those based on measured values of operating parameters) are called "measured values". The measurements of the operating parameters and / or the measured values for the performance indicators may be communicated 512 as shown to both the power plant controller 505 and the tuning module 503. The tuning module 503 may be configured to receive feedback from, as discussed in greater detail below to compute a data reconciliation or tuning process for use in tuning the power plant model 502 to configure the tuned power plant model 507.
The power plant model 502 may, as discussed, be a computer aided model configured to simulate the operation of the power plant 501. According to the present method, the power plant model 502 may be configured to simulate power plant operation that corresponds to the first operating period of the power plant 501. To accomplish this, the power plant model 502 may be provided with information and data concerning the operating parameters of the first operating period. While this information may include any of the operating parameters measured during the first operating period, it is understood that the input data for the power plant model 502 may be limited to a subset of the measured operating parameters. Thus, the power plant model 502 can then be used to calculate values for selected operating parameters that were excluded from the input data set. More specifically, the power plant model may be provided with input data for the simulation that includes many of the values measured for the operating parameters but from which certain measured values for selected operating parameters have been omitted. The simulation may be configured to predict as an output a simulated value for the selected operating parameters. The present method may then use the simulated values to predict values for the performance indicators. In this case, these values for the performance counters are called the "predicted values". Thus, the measured values for the performance indicators, which were determined directly from measured power plant operating parameters, may have corresponding predicted values. As illustrated, the predicted values for the performance indicators may be communicated 514 to the tuning module 503.
The tuning module 503 may be configured to compare the corresponding measured and predicted values for the performance indicators to determine a difference between them. It is understood that the difference thus calculated reflects an error level between actual power (or its measurements) and power simulated by the power plant model. The power plant model 502 may be tuned based on this difference or feedback 515. In this way, the tuned power plant model 507 is configured. The tuned power plant model 507, which may also be called an offline or predictive model, may then be used to determine optimized operating modes for a subsequent operating period by simulating suggested or possible operating modes. The simulations may include estimates or forecasts of future unknown operating conditions, such as environmental conditions. It should be understood that the optimization may be based on one or more performance objectives 516 in which a cost function is defined. As illustrated, the performance objectives 516 may be communicated to the optimizer 510 by the power plant operator module 509.
The process of tuning the power plant model may be configured as a repeating process having multiple steps. It is understood that power plant model 502 may, according to certain embodiments, include algorithms in which logical statements and / or parameterized equations correlate process inputs (ie, fuel supply, air supply, etc.) to process outputs (generated electricity, power plant efficiency, etc.). The step of tuning the power plant model 502 may include adjusting one of the algorithms in the power plant model 502 and then simulating the operation of the power plant 501 during the first operating period using the adjusted power plant model 502 to determine the effect that the adjustment had , More specifically, the predicted value for the performance indicator may be recalculated to determine the effect the adjustment to the power plant model had on the calculated difference. If it turns out that the difference in using the adjusted power plant model 502 is less, the power plant model 502 may be updated or "tuned" to have this setting on resuming. Further, it will be understood that the power plant model 502 may be constructed with multiple logical statements having power multipliers used to reflect changes in the way the power plant operates under certain conditions. In such cases, tuning the power plant model 502 based on the calculated difference may include the steps of: a) making adjustments to one or more of the power multipliers; b) simulating the operation of the power plant during the first period of operation with the power plant model 502 having the adjusted power multiplier, and c) recalculating the predicted value for the power indicator using the power plant model 502 as set by the power multiplier to determine whether the recalculation results in a reduced difference. These steps can be repeated until a setting made on one of the performance multipliers results in decreasing the difference, which would indicate that the model is more accurately simulating the current performance. It will be understood that the power multiplier may refer, for example, to expected performance degradation based on accumulated operating hours of the power plant. In another example, where the performance indicator includes generating capacity, the step of tuning the power plant model 502 may include recommending adjustments to factors based on a difference between a measured generation capacity and a predicted generation capacity. Such adjustments may have changes that ultimately result in the predicted generation capacity being substantially equal to the measured generation capacity. The step of tuning the power plant model 502 may thus include changing one or more correlations within the power plant model 502 until the predicted or simulated value for a performance indicator is substantially equal to (or within a margin of) the measured value for the performance indicator.
After tuning, the method may then use the tuned model 507 to simulate the proposed operation of the power plant. According to certain embodiments, a next step of the present method comprises determining which simulated operation is preferable in view of set performance objectives 516. In this way, optimized operating modes of the power plant can be determined. According to a preferred embodiment, the determination process of an optimized operation mode may comprise several steps: First, several proposed operation modes may be selected or selected from among many possible ones. For each of the proposed modes of operation, corresponding suggested parameter sets 517 may be generated for the second operating period. As used herein, a parameter set defines values for a plurality of operating parameters such that the parameter set collectively defines or describes aspects of a particular operating mode. The suggested parameter sets may therefore be configured to describe or relate to many of the possible operating modes of the power plant 501, and may be configured as input data sets for the tuned power plant model 507 to simulate operation. Once the operating parameters are generated and arranged in the suggested parameter sets, the tuned power plant model 507 may simulate the operation of the power plant 501 according to each set. The optimizer 510 may then judge the results of the simulated operation 519 for each of the proposed parameter sets 517. The assessment may be performed according to the performance objectives defined by the power plant operator and the cost functions defined therein. The optimization process may include any of the methods described herein.
Cost functions defined by the performance objectives may be used to assess economic performance of the simulated operation of the power plant 501 during the second operating period. Based on the judgments, one of the proposed parameter sets may be considered to be generating as a simulated operation that is preferable to that generated by the other suggested parameter sets. According to the present invention, the mode of operation corresponding to or described by the proposed parameter set generating the most preferred simulated operation is called the optimized mode of operation. Once determined, as discussed further below, the optimized mode of operation may be passed to a power plant operator for consideration or communicated to the power plant operator for automated implementation.
In accordance with a preferred embodiment, methods of the present invention may be used to assess specific modes of operation to determine and recommend preferred alternatives. It is understood that generating units of the power plant 501 are controlled by actuators having variable setpoints controllably connected to a control system, such as the power plant control device 505. The operating parameters of the power plant 501 can be categorized into three categories: manipulated variables, Fault variables and controlled variables. The manipulated variables relate to controllable process inputs that can be manipulated by actuators to control the controlled variables, while the disturb variables affect uncontrollable process inputs affecting the controlled variables. The controlled variables are the process outputs that are controlled relative to defined target levels. According to preferred embodiments, the control method may include receiving predicted values for the disturbance variables for the second operating period (that is, the operating period for which an optimized operating mode is calculated). The disturbance variables may include environmental conditions, such as ambient temperature, pressure and humidity. In such cases, the suggested parameter sets generated for the second operating period may include values for the disturbance variables associated with the predicted values for the disturbance variables. More specifically, the generated values for each environmental condition parameter may include a range of values for each of the environmental condition parameters. For example, the area may have a low case, a middle case, and a high case. It is understood that having multiple cases may allow a power plant operator to plan for best / worst case scenarios. The predicted values may have probability scores corresponding to the various cases, which may further assist the operator of the plant in planning different operating liabilities and / or hedges.
The step of generating the proposed parameter sets may include generating target levels for the controlled variables. The target levels may be generated to correspond to competing or alternative modes of operation of the power plant 501, and may include operator input. Such operator inputs may be initiated by the power plant operator module 509. According to a preferred embodiment, such goal levels may include a desired level of spending for the power plant 501 that may be based on likely expense levels in view of past power plant usage patterns. As used herein, "spending level" represents a load level or level of electricity generated by power plant 501 for commercial distribution during the second operating period. The step of generating the proposed parameter sets may include generating a plurality of cases in which the output level remains the same or constant. Such a constant level of expenditure may reflect a base load for the power plant or a set of generating units. Multiple target levels may be generated, each corresponding to a different commitment level from each of the generating units, and these may be drawn to probable modes of operation in light of historical usage. The method may then determine the most efficient mode of operation given the known limitations. In addition, the proposed parameter sets may be generated such that the disturbance variables maintain a constant level for the multiple instances generated for each target level. The constant level for the disturbance variables may be based on predicted values received. In such cases, in one aspect of the present invention, the step of generating the proposed parameter sets includes generating a plurality of cases wherein the manipulated variables are varied over ranges to determine an optimized operating mode for achieving a baseload level in view of the predicted or expected environmental conditions. According to exemplary embodiments, the cost function is defined as a power plant efficiency or a heat cost coefficient or may include a direct economic indicator, such as operating costs, income or profit. Thus, the most efficient method of controlling the power plant 501 may be determined in situations where a base load is known and interference variables having a relatively high level of precision can be predicted. The optimized operating modes determined by the present invention in such cases may be configured to have a specific control solution (ie, specific setpoints and / or ranges therefor for the actuators that control the manipulated variables of the power plant) that are derived from the Power plant control device 505 may be used to achieve optimum performance. Thus, the control solution represents the optimized mode of operation for meeting a defined or contracted base load, given the values predicted for the various interfering variables. This type of functionality can serve as an interday or inter-market period optimizer or as a test that / the ongoing operations are analyzed in the background to find more efficient operating modes that still meet predefined load levels. As the market time span covered by the previous allocation offer progresses, environmental conditions may become known, or at least the level of confidence in its prediction may increase more precisely than what was estimated during the bidding process. Thus, the present method can be used to optimize control solutions to cover allocated load given the certain knowledge of environmental conditions. This particular functionality is illustrated in FIG. 17 as the second parameter sets 517 and the simulated operation 519 in conjunction with the second parameter sets 517. As such, the optimization process of the present invention may also include a "fine-tuning" aspect, with which simulation runs on the tuned power plant model 507 may provide more efficient control solutions that can then be communicated to and implemented by the power plant control device.
Another aspect of the present invention involves its use for optimizing fuel purchasing for the power plant 501. It is understood that power plants typically make regular fuel purchases from fuel markets that operate in a particular way. In particular, such fuel markets are typically run on a prospective basis in which power plants 501 predict the amount of fuel required for a future operating period and then make purchases based on the prediction. In such systems, power plants 501 strive to maximize profits by maintaining low fuel stocks. Power plants 501, however, regularly purchase extra quantities of fuel to avoid the costly situation of having an inadequate supply of purchased fuel for generating the amount of electricity that the power plant contracted to supply during the allotment process. This type of situation can occur when changing environmental conditions result, for example, in less efficient power generation than predicted or the actual generating capacity of the power plants is overestimated. It is understood that several aspects of the present application that have already been discussed may be used to determine an optimized mode of operation, and thus to calculate a high-precision fuel supply forecast. The present optimization processes may therefore provide a more accurate prediction associated with power plant efficiency and load capacities that may be used to estimate the amount of fuel required for a future operating period. This will allow power plant operators to maintain a tighter fuel purchasing margin, which will benefit the plant's economic performance.
The present invention according to an alternative embodiment comprises a method for optimizing power plant performance in which a prediction horizon is defined and used in the optimization process. It will be appreciated that a prediction horizon is a future operating time period that is divided into regularly repeating intervals for the purpose of determining an optimized operating mode for an initial time interval of the prediction horizon. In particular, the operation of the power plant is optimized by optimizing the power over the entire forecast horizon, which is then used to determine an optimized operating mode for the initial time interval. It should be understood that the process is then repeated to determine how to operate the power plant during the next time interval, which, as will be understood, becomes the initial time interval relative to this next repetition of the optimization cycle. For this subsequent optimization, the forecast horizon may remain the same, but is redefined in relation to what is now defined as the initial time interval. This means that the forecast horizon is effectively pushed forward into the future by an additional time interval at each repetition. As already mentioned, a "suggested parameter set" relates to a data record which has values for a plurality of operating parameters and thereby defines or describes one of the possible operating modes for the power plant 501. According to a preferred embodiment, the process of determining the optimized mode of operation in cases involving a prediction horizon may include one or more of the following steps: First, several suggested horizon parameters are generated for the prediction horizon. As used herein, a "suggested horizon parameter set" has a suggested parameter set for each of the time intervals of the prediction horizon. For example, a 24-hour forecast horizon may be defined as having 241-hour time intervals, meaning that the proposed horizon parameter set has proposed parameter sets for each of the 24 time intervals. As a next step, the proposed horizon parameter sets are used to simulate operation during the forecast horizon. Then, for each of the simulation runs, the cost function is used to assess economic performance to determine which of the proposed horizon parameter sets is the least expensive or, as used herein, an "optimized horizon simulation run". According to exemplary embodiments, the operating mode described within the optimized horizon simulation run for the initial time horizon of the prediction horizon may then be referred to as the optimized operating mode for the operating time period corresponding to the initial time interval. The optimization process can then be repeated for subsequent time intervals. The present invention may include receiving predicted values for the disturbance variables for each of the time intervals defined within the prediction horizon. The proposed horizon parameter sets may then be generated such that the proposed parameter set corresponding to each of the time intervals has values for the disturbance variables that relate to predicted values received for the disturbance variables.
It is understood that proposed horizon parameter sets can be generated in such a way that they cover a range of values for the disturbance variables. As above, the range may have multiple cases for each of the confounding variables and may have high and low values, each representing cases above and below the predicted values. It will be appreciated that according to any of the described embodiments, the steps of simulating operating modes and determining therefrom optimized operating modes may be repeated and configured in a repeated process. As used herein, each repetition is called an "optimization cycle." It will be understood that any repetition may include defining a subsequent or next operating time period for optimization. The subsequent period may occur just after the operating period optimized by the previous cycle, or may have an operating period corresponding to a future period, as may be the case, for example, when the present method for preparing allocation offers or consulting regarding the economic impact of alternative maintenance planning.
The steps of tuning the power plant model 502 may be repeated to update the tuned power plant model 507. Thus, a tuned power plant model 507 that reflects a recent tune can be used with optimization cycles to produce more effective results. According to alternative embodiments, the optimization cycle and the tuning cycle of the power plant model 502 may be separated with respect to each other such that each cycle is cycled according to its own design. In other embodiments, the power plant model 502 may be updated or tuned after a predetermined number of repetitions of the optimization cycle. The updated tuned power plant model 507 is then used in subsequent optimization cycles until the predetermined number of repetitions occur so that another tuning cycle is initiated. In certain embodiments, the tuning cycle occurs after each optimization cycle. According to alternative embodiments, the number of optimization cycles that initiate tuning of the power plant model 502 is associated with the number of time intervals of the prediction horizon.
As noted, the present invention may optimize the operation of power plants 501 in accordance with performance objectives that may be defined by the power plant operator. According to preferred embodiments, the present method is used to economically optimize the operation of the power plant. In such cases, the performance objectives have and define a cost function that provides the criteria for economic optimization. According to example embodiments, the simulated operation has predicted values for selected performance counters for each of the proposed parameter sets as an output. The cost function may include an algorithm that correlates the predicted values for the performance indicators with operating costs or any other indication of economic performance. Other performance indicators that may be used include, for example, a power plant heat input coefficient and / or a fuel consumption. According to alternative embodiments, simulation outputs include predicted values for hot gas path temperatures for one or more of the heat generating units of the power plant 501 that may be used to calculate spent component life costs. These costs reflect predicted degradation costs associated with the hot gas path components resulting from the simulated operation. The cost function may further include an algorithm that correlates predicted values for the performance indicators with operating revenue. In such cases, the operating revenues may then be compared to operating costs to reflect a net revenue or profit for the power plant 501. The present method may further predict the step of receiving a predicted price for electricity sold within the market during the optimization period, and the selected performance indicators may have an electricity output level that may then be used to calculate expected operating revenue for the upcoming operating period , Thus, the present method can be used to maximize economic viability by comparing operating costs and revenue.
It is understood that performance objectives may be further defined to include selected operability constraints. According to certain alternative embodiments, the present method includes the step of disqualifying any of the suggested parameter sets that produce simulated operation that violates any of the defined operability constraints. Operability limitations may include, for example, emission thresholds, maximum operating temperatures, maximum mechanical stress levels, etc., as well as legal or environmental regulations, contract terms, safety regulations, and / or machine or component operability thresholds and limits.
As already mentioned, the present method comprises generating suggested parameter sets 517 which describe alternative or possible operating modes of the power plant 501. As illustrated, the suggested parameter sets 517 may be generated in the power plant operator module 509 and may include inputs from a power plant manager or human operators. More generally, possible modes of operation may be considered as competing modes for which simulation is performed to determine the mode of operation that best meets the performance objectives and expected conditions. According to exemplary embodiments, these alternative modes of operation may be selected or defined in several ways. According to a preferred embodiment, the alternative operating modes have different output levels for the power plant 501. Expenditure level, as used herein, refers to the level of electricity generated by the power plant 501 for commercial distribution within the market during a defined market period. The suggested parameter sets can be configured to define multiple cases at each of the different output levels. Multiple levels of spending may be covered by the suggested sets of parameters, and the selected ones may be configured to match a range of possible outputs for the power plant 501. In particular, due to the multiple generating units of the power plant and the scalability limitations associated therewith, the proposed parameter sets may be grouped or concentrated at levels that are more achievable or, preferably, in view of the particular configuration of the power plant 501.
As mentioned, each of the competing modes of operation may include multiple cases. For example, if the competing modes of operation are defined at different levels, the multiple cases may be selected to reflect a different way of achieving the level of spending. If the power plant has multiple generating units, the multiple cases at each output level can be distinguished by how each of the heat generating units is operated and / or deployed. According to one embodiment, the different generated cases are distinguished by varying the percentage of the initial level provided by each of the generating units. The power plant 501 may include, for example, a combined cycle power plant 501 in which heat generating units comprise gas and steam turbines. In addition, the gas and steam turbines may each be extended by an intake conditioning system such as a refrigeration unit and a HRSG pipe firing system. It will be understood that, for example, the intake conditioning system may be configured to cool intake air of the gas turbine to boost its generation capacity, and that the HRSG pipe heating system may be configured as a secondary heat source for the boiler to boost the steam turbine's generation capacity. According to this example, the heat generating units comprise the gas turbine or alternatively the gas turbine boosted by the intake conditioning system, and the steam turbine or alternatively the steam turbine boosted by the HRSG pipe firing system. The multiple cases covered by the proposed parameter sets may therefore have instances in which these particular heat generation units are deployed in different ways while still meeting the different output levels selected as competing modes of operation. The simulated operation can then be analyzed to determine which reflects an optimized mode of operation according to a defined criterion.
According to an alternative embodiment, the proposed parameter sets may be pulled to different operating modes to calculate economic gains from maintenance operations. To accomplish this, one of the contending modes of operation may be defined as one in which the maintenance operation is assumed to be completed before the period of operation selected for optimization. This mode of operation can be defined to reflect a performance boost that is expected to accompany the completion of this maintenance operation. An alternate mode of operation may be defined as one in which the maintenance operation is not performed, meaning that the simulation of the multiple cases for that mode of operation would not have the expected performance overhead. The results from the simulations can then be analyzed to better understand the economic effects, and the multiple cases can be used to show how different scenarios (such as fluctuations in fuel prices or unexpected environmental conditions) affect the outcome. It should be understood that using the same principles, the competitive modes of operation may include a Abregelmodus and a Abschaltmodus.
The present invention also has various ways in which the optimization process can be used by the power plant operators to automate processes and improve efficiency and performance. In one embodiment, as illustrated in FIG. 17, the method includes the step of communicating a calculated optimized operating mode 521 to the power plant operator module 509 for approval by a human operator before the power plant 501 is controlled according to the optimized mode of operation. In a consultant mode, the present method may be configured to present alternative modes of operation and the economic ramifications associated with each to alert the power plant operator to such alternatives. Alternatively, the control system of the present disclosure may function to implement automatically optimized solutions. In such cases, the optimized mode of operation may be electronically communicated to the power plant controller 505 to cause control of the power plant 501 in a manner consistent therewith. In power systems having an economical allocation system for distributing power generation to a group of power plants 501, the optimization method of the present invention may be used to generate precise and competitive bids for submission to the central authority or dispatcher. As one of ordinary skill in the art appreciates, the optimization features already described may be used to generate bids that reflect real generation capacity, efficiency, heat cost coefficients while also providing useful information to power plant operators in the context of economic compromises that the power plant in future market periods by selecting between different operating modes. Increased precision of this kind and additional analysis help to ensure that the power plant remains competitive in the bidding process while also minimizing the risk of highly unprofitable allotment results due to unforeseen eventualities.
FIGS. 18 to 21 illustrate exemplary embodiments of the present invention relating to shutdown and / or shutdown operation of a power plant. The first embodiment, as illustrated in flowchart 600 of FIG. 18, which may be called a "scheduling advisory", teaches methods and systems for simulating and optimizing a power plant trim level during a defined or selected operating period ("selected operating period"). In preferred embodiments, the present method is used in power plants having multiple gas turbines that may have combined cycle power plants having gas turbines and one or more steam turbines. The tuned power plant model may be used to determine an optimized minimum load to operate the power plant and a trim level during the selected operating period. As mentioned above, an "optimized" mode of operation may be defined as one that is considered preferred or rated over one or more other possible modes of operation. An operating mode for the purposes of these embodiments may include an allocation of particular power generating units to meet a load obligation or other performance objectives, as well as the physical configurations of the generating units within the power plant. Such functionality means that upon achieving an optimized or improved mode of operation, the present invention can accommodate multiple power plant combinations that can accommodate the various trim configurations of each generating unit as well as configurations that disable one or more of the units while others remain operational at full or trim levels. The method may further consider other limitations such as operability limitations, performance objectives, cost functions, operator input, and environmental conditions in its calculation of an improved power plant trim mode that improve performance and / or efficiency. The present method as outlined herein and / or outlined in the appended claims may take current and predicted environmental conditions to optimize the Abregelbetriebsmodus and changing the unit configuration and / or control such that the operation of one or more of the generating units is set dynamically, if current conditions differ from those predicted. In a preferred embodiment, such power is at least partially defined as that which minimizes the level of fuel usage or consumption during the proposed trim drive period of time.
The adjustment advisor of the present invention may take into account several factors, criteria, and / or operating parameters in achieving an optimized or improved exercise solution and / or recommended control action. In preferred embodiments, these include, but are not limited to, the operating limits of the gas turbine engine (ie, temperature, aerodynamics, fuel splits, lean combustion chamber quench, mechanical and emission limitations); Gas turbine and steam turbine control systems; Steam turbine at minimum throttle temperature; maintenance of the vacuum seal and / or the condenser as well as other factors such as the configuration or installation of systems or their control. One of the outputs of the optimization may include a recommended mode of operation and a recommended configuration of the power plant or multiple power plants, the plurality of different power types including wind power plant, solar power plant, piston engine, nuclear power plant, and / or other types. It is understood that the recommended operating mode is automatically initiated or electronically communicated to a power plant operator for approval. Such control may be implemented by off-site or on-premises control systems configured to control the operation of the generating units. In addition, in situations where the power plant has multiple gas turbine engines, the output of the present process may include identifying those of the gas turbines that should continue to operate and which should be shut down during the settling period, which is a process described with reference to FIG. 19 is described in more detail. For each of the gas turbines recommended by the consultant during the off-period for continued operation, the present method may further calculate a load level. Another output may calculate calculating the total load for the power plant during the settling time period as well as the hourly target load profile based on the predicted environmental conditions which, as mentioned, may be adjusted as conditions change. The present invention may also calculate the predicted fuel consumption and emissions of the power plant during the ramp down period. The output of the disclosed method may include the operational set-up / configuration in view of the control setpoints available to the generating units and the power plant to more efficiently achieve the target generation levels.
As discussed above, traders and / or power plant managers (hereafter referred to as "power plant operators"), other than being bound by pre-existing contract clauses, typically provide their power plants in a future market such as, for example a day ahead market. As an additional consideration, power plant operators have the task of ensuring that adequate fuel supply is maintained so that the power plant is capable of meeting target or contractual generation levels. However, in many cases fuel markets work prospectively, so that advantageous pricing conditions are available to power plants willing or able to commit to future fuel purchases in advance. More specifically, the further the fuel is purchased in advance, the more advantageous the pricing. In view of these market dynamics, in order for a power plant to achieve an optimized or high level of profitability, power plant operators must compete with other generation units to exploit its generation capacity while also accurately estimating the fuel required for future generation periods, so that: 1) the fuel is purchased in advance to ensure lower pricing, and 2) a large fuel buffer stock is not required so that low fuel inventory can be maintained. If done successfully, the power plant operator ensures better pricing by committing early on to future fuel purchases while at the same time not being over-bought, so that unnecessary and costly fuel reserves are required, or insufficiently purchased, thus risking a fuel shortage.
Methods of the present invention may optimize or improve the efficiency and profitability of power generation activities by specifying an IHR profile for a generating unit or particular configuration of a power plant, particularly where these relate to the preparation of an allotment bid to secure generation market share. The present method may include specifying optimal generation allocation for multiple generating units within a power plant or multiple power plants. The present method may take into account the operating and control configurations available to these generating units, interchange the possible arrangements, and thereby achieve an offer that, if selected, allows for power generation during the offer period at a reduced or minimized cost. The present process may take into account all applicable physical, regulatory and / or contractual restrictions. As part of this overall process, the present method can be used to optimize shutdown and shutdown operation for a power plant having multiple generating units. This approach may include considering expected exogenous conditions, such as weather or environmental conditions, gas quality, reliability of the generating units, and ancillary obligations, such as steam generation. The present method can be used to enumerate IHR profiles for multiple generation units having multiple configurations, as well as to control settings for the selected Abregelkonfiguration and then to control the expected exogenous conditions in preparing the allocation offer of the power plants.
A common decision for operators concerns whether the power plant is shut down or shut down during off-peak hours, such as during the night when demand or load requirements are minimal. It will be understood that the result of this decision will depend significantly on the understanding of the power plant operator of the economic branches associated with each of these possible operating modes. In certain cases, the decision to turn off the power plant may be readily apparent, while the optimum minimum load at which the power plant is to be maintained during the settling period remains uncertain. Although the power plant operator has decided to regulate the power plant for a certain period of time, the operator is unaware of the ramping operating points at which the various generating units of the power plant are to be operated in the most cost effective manner.
The Abregelberater of Fig. 18 can be used as part of a process for recommending an optimal minimum load at which the power plant is to operate. This advisory function may also recommend the best course of action for the power plant given a specific scenario of environmental conditions, economic inputs and operating parameters and constraints. From these inputs, the process may calculate the best operating levels and then recommend the operating parameters required to control the power plant, as discussed in more detail with reference to FIG. 19. It is understood that this functionality can result in several side benefits, including extended part life, more efficient off-mode operation, improved economic performance, and improved fuel purchasing precision.
As illustrated in flowchart 600, certain information and relevant criteria may be collected during the initial steps. At step 602, data, variables, and other factors associated with power plant systems and generating units may be determined. These may include any of the factors or information listed above. In accordance with a preferred embodiment, an environmental profile may be received that may include a prediction of environmental conditions during the selected operating time period. Relevant emission data, which may include emission limits and past emissions for the power plant, may also be collected as part of this step. Another factor includes data related to the potential sale of electricity and / or steam during the selected operating period. Other variables that may be determined as part of this step include the number of gas turbines in the plant, the combustion and control systems for each of the gas turbines, as well as any other factory-specific constraints that may be relevant to the calculations discussed below.
At step 604, the period of the proposed Abregelbetriebs (or the "selected operating period") can be defined with specificity. It should be understood that this may be defined by a user or power plant operation and has a selected operating period during which the analysis of available stall modes is desired. The definition of the selected operating period may include its expected length and a user-specified start time (that is, the time at which the selected operating period begins) and / or a stop time (that is, the time at which the selected operating period ends). This step may further include defining an interval within the selected operating period. The interval may be configured to divide the selected operating time period into a plurality of sequential and periodically spaced periods. For. the example provided here defines the interval as one hour, and the selected operating period is defined as having several of the one-hour intervals.
At step 606, the number of gas turbines involved in the optimization process for the selected operating period may be selected. This may include all gas turbines of the power plant or a portion thereof. The method may further include considering other generating units of the power plant, such as steam turbine systems, and taking into account their operating conditions during the selected operating period, as described in greater detail below. Determining the gas turbines involved in the trim process may include polling or receiving input from the power plant operator.
At step 608, the present method may configure an interchange matrix in view of the number of gas turbines that were determined as part of the proposed tuning process during the selected operating period. It will be appreciated that the interchange matrix is a matrix that may include the different ways in which the plurality of gas turbine engines may be used or operated during the selected operating period. For example, as illustrated in the example permutation matrix 609 of FIG. 18, in the case of two gas turbines, the interchange matrix has four different combinations covering each of the possible configurations. In particular, if the power plant has a first and a second gas turbine, the commutation matrix has the following series or cases:a) both the first and the second gas turbine are "on", that is, they are operated in a Betriebsabregelzustand;2) both the first and second gas turbines are "off", that is to say they are operated in an operating shutdown state;3) the first gas turbine is «on», and the second gas turbine is «off», and 4) the first gas turbine is «off» and the second gas turbine is «on». It will be appreciated that only two permutations are possible in the case of a single gas turbine while for three gas turbines seven different series or cases would be possible, each of which has a different configuration for how the three gas turbine engines will operate during a given time frame "On" and "Off" can be used. Referring to Figure 17 and the optimization process discussed in the associated text, each case or row of the interchange matrix may be considered to represent a different or competing mode of operation.
As part of the steps represented by steps 610, 613, 614, 616, and 618, the present method may configure proposed parameter sets for the proposed trim action. As indicated, the selected operating time period can be divided into several one-hour time intervals. The process of configuring the suggested parameter sets may begin at step 610, where it is determined whether each of the intervals has been handled. If the answer to this question is yes, the process may proceed as illustrated to an issue step (ie, step 611) where the output of the settling analysis is provided to an operator 612. If not all intervals have been covered, the process may proceed to step 613 where one of the uncovered intervals is selected. Then, the environmental conditions may be selected at step 614 for the selected interval based on received forecasts. Continuing at step 616, the process may select a row from the interchange matrix and at step 618 set the on / off state of the gas turbines according to the particular row.
From here, the present process can continue according to two different routes. In particular, the method may continue with an optimization step illustrated by step 620, while also continuing a decision step at step 621, where the process determines whether all interchanges or rows of the interchange matrix have been covered for the selected interval. If the answer to this is "No", the process may go back to step 616 where a different commutation series is selected for the interval. If the answer to this is yes, the process as illustrated may proceed to step 610 to determine if all intervals have been covered. It is understood that once all rows of the interchange matrix have been treated for each interval, the process may proceed to the output step of step 611.
At step 620, the present method may optimize performance by using the tuned power plant model, as previously discussed in FIG. 17. In accordance with this approach, multiple cases may be created for each of the contending modes of operation, that is, each of the rows of the interchange matrix for each of the intervals of the selected operating time period. According to a preferred embodiment, the present method generates suggested parameter sets in which a plurality of operating parameters are varied to determine the effect on a selected operating parameter or performance indicator. For example, in accordance with this embodiment, the proposed parameter set may include manipulating inlet guide vane ("IGV") and / or turbine exhaust temperature ("Texh") to determine which combination is a minimized total fuel burn rate for the power plant of the on / off state of the particular row and the environmental conditions predicted for the particular interval. It will be appreciated that the operation that minimizes fuel consumption while meeting the other restrictions associated with the trim mode is one that can be used to economically optimize or at least economically improve off-line performance relative to one or more alternative operating modes.
As shown, cost functions, performance objectives and / or operability limitations of the present invention may be used during this optimization process, according to certain embodiments. These may be provided by a power plant operator represented by step 622. These limitations may have limitations associated with the settings of IHR, Texh limits, burn limits, etc., as well as those associated with the other heat systems that may be part of the power plant. For example, in power plants having combined cycle systems, the operation or maintenance of the steam turbine during the shutdown process may have certain limitations, such as maintaining a minimum steam temperature or a condenser vacuum seal. Other operability limitations may include the logic required that certain subsystems may be affected in certain operating modes and / or that certain subsystems may be mutually exclusive, such as evaporative coolers and refrigeration units.
Once the present method has iterated through the iterations given the intervals and the different rows of the interchange matrix, the results of the optimization may be communicated to the power plant operator at step 611. These results may have an optimized case for each of the rows of the interchange matrix for each of the time intervals. As an example, the output describes optimized operation defined by a fuel consumption cost function for the power plant for each of the permutations for each of the intervals. In particular, the output may have the required minimum fuel (as optimized using the tuned power plant model according to the methods already described) for each of the possible power plant configurations (as represented by the rows of the swapping matrix) for each interval, while also including the operability limitations, performance objectives, and expected Environmental conditions are met. According to another embodiment, the output has an optimization that minimizes a generation output level (i.e., megawatts) for the potential power plant configurations for each of the intervals in the same way. It should be understood that certain of the possible power plant configurations (as represented by permutations of the swapping matrix) may not be able to meet the operability constraints, regardless of the fueling to generate the output level. Such results may be eliminated and disregarded or reported as part of the output of step 611.
Figures 19 and 20 graphically depict ways in which a gas turbine of a power plant may be operated during a selected period of operation having defined intervals (in Figures "I") in view of typical transient operation constraints. It will be appreciated that transient operation includes switching a generating unit between different operating modes, including those involving transition to or from a shutdown mode of operation. As shown, multiple operational paths or sequences 639 may be obtained, depending on: 1) a gas turbines initial state 640; and 2) decisions made in connection with whether to change operating modes at intervals where changes are in consideration of transient operating limitations possible are. It should be understood that a plurality of different sequences 639 represent the multiple ways in which the generating unit can operate during the intervals shown.
It will be understood that the output of the method of FIG. 18, coupled with diagrams of FIGS. 19 and 20, may be used to configure proposed trim control sequences for power plant generation units. FIGS. 19 and 20 illustrate examples of how a generating unit of a power plant can be used and how its operating modes are changed in the course of lapse of time intervals, which may include cases in which the operating mode of the generating unit remains unchanged, cases in which Operation mode of the unit is changed from a Abschaltbetriebsmodus to a Abregelbetriebsmodus, and cases in which the operating mode of the unit is changed from a Abschaltbetriebsmodus to a Abregelbetriebsmodus. As illustrated, the transient operating restriction used in this example, changing an operating mode, requires the unit to remain in the changed operating mode for a minimum of at least two intervals. The many sequences (or paths) by which the generating unit gets to the last interval represent the possible tuning operation sequences available to the unit in view of the transient operating constraints.
It will be appreciated that the analysis results of Figure 18, that is, the optimized trim operation for each of the matrix exchanges, may be used to select from among the possible trim operation sequences a plurality of preferred cases that may be called proposed trim operation sequences. In particular, given the results of the method described in connection with FIG. 18, the proposed trim mode sequences may be selected from trim mode traps that meet power plant performance objectives and constraints while also providing power according to a selected cost function (such as MW output or fuel consumption). is optimized. The considerations illustrated in Figures 19 and 20 illustrate one way of determining whether tailing operation sequences are achievable in view of transient operating constraints. The proposed tuning operation sequences which are arrived at by the combined analysis of Figures 18 to 20, are operational sequences associated with time limitations associated with transferring a unit from one mode of operation to another.
Referring to Fig. 21, there is provided a method for further modeling and analyzing the shutdown operation of a power plant. It is understood that this method can be used to analyze penalty costs as compared to shutdown costs for specific cases involving a single generating unit during a defined time interval. However, it may also be used to analyze power plant level costs in which a recommendation is sought in relation to types that can be controlled to operate multiple generating units during a selected operating time period having multiple intervals. Thus, the output of FIGS. 18 and 20 may be assembled to configure possible modes of operation or sequences during the span of multiple intervals which, as set forth, may then be analyzed according to the method of FIG. 21 to provide a more complete understanding of the invention Abregelbetriebs provide during a longer period of operation.
Power plant operators must, as previously discussed, regularly decide between stall and shutdown modes during off-peak hours. Although certain conditions make the decision uncomplicated, it is often difficult, especially in view of the increased complexity of the modern power plant and the several heat generating units they usually contain. It is understood that the decision to shut down a power plant is significantly dependent on a full assessment of the economic benefits associated with each mode of operation. The present invention can be used in accordance with the alternative embodiment of power plant operators illustrated in FIG. 21 to gain an improved understanding of the trade-offs associated with each of these different modes of operation to improve decision-making. According to certain embodiments, the method of FIG. 21 may be used in tandem with the scheduling advisor of FIG. 18 to enable a combined advisor function that: 1) best practices between stall and shutdown modes for the generating units of the power plant in view of known conditions and economic factors, and 2) if a shutdown operation is the best course of action for some of these units, the minimum standard load level that is optimal recommends. Thus, power plant operators can more easily identify simple situations in which the units of the power plants should be shut down rather than turned off, and vice versa, based on which represents the best economic course of action for the power plant in view of a specific scenario of environmental conditions, economic inputs and operating parameters. Side benefits, such as extending component life, are also possible. It should also be understood that the methods and systems described in connection with Figs. 18 and 21 may be used separately.
In general, the method of flowchart 700, which may also be part of or is referred to as an "adjustment advisor," applies user inputs and analytical process data such that calculations are performed, the costs associated with power plant shutdown judge in comparison to his shutdown. It should be understood that flowchart 700 of FIG. 21 provides this advisory feature by taking advantage of the tuned power plant model discussed in detail above in accordance with certain preferred embodiments. As part of this functionality, the present invention may advise on the different results, whether economic or otherwise, between stalling and shutting down a power plant during off-peak periods. The present invention may provide relevant data clarifying whether the power plant shutdown is to be preferred over its shutdown during a specified market time period. According to certain embodiments, the operation, which has the lower cost, may then be recommended to the power plant operator as an appropriate action, although, as also presented herein, side problems or other considerations that may affect the decision may also be communicated to the power plant operator. The present method can produce potential costs as well as the likelihood of such costs occurring, and these considerations can affect the ultimate decision as to which is the preferred mode of operation. Such considerations may include, for example, a complete analysis of both the short term cost of ownership and the long term cost of ownership associated with the plant's maintenance, operating efficiencies, emission levels, equipment retrofits, etc.
It should be understood that the adjustment advisor may be implemented using many of the systems and methods described above, particularly those discussed in connection with FIGS. 16-20. The scheduling advisor of FIG. 21 may collect and use one or more of the following types of data: user specified start and stop time for the proposed stall operation period (ie, the period of time for which the stall operation mode is analyzed or considered); Fuel costs, ambient conditions, time except switches, alternative uses of electricity, sale / price of electricity or steam during the relevant period of time; Operating and maintenance costs during the period; User input; calculated drop-off load; predicted emissions for operation; current emission levels delivered by the power plant and limits for the defined regulatory periods; Specifications in connection with the operation of the starting device; Regulations and equipment related to drainage processes; Fixed costs for modes of fuel operation; Costs in connection with the starting process; Factory-Anfahrzuverlässigkeit; unbalanced loads or penalties for delayed start-up; Emissions in connection with starting up; Fuel rate used for the auxiliary boiler if a steam turbine is present, and historical data related to how the plant's gas turbines have operated at cut-off or shut-down operating modes. In certain embodiments, the outputs of the present invention, as discussed below, may include: a recommended mode of operation (ie, stall and shutdown mode) for the power plant during the relevant time period; Costs associated with each mode of operation; a recommended factory load and a time load profile; a recommended time to initiate the launch of the unit and emissions that have been consumed since the beginning of the year and emissions that remain for the remainder of the year. According to certain embodiments, the present invention may calculate or predict fuel consumption and emissions of the power plant during the relevant time period, which may then be used to calculate abatement costs as compared to the shutdown cost for one or more particular gas turbine engines. The present method may use the cost of each gas turbine in the shutdown and shutdown modes to determine the combination that has the minimum operating cost. Such optimization may be based on different criteria that may be defined by the power plant operator. For example, the criteria may be based on income, net income, emissions, efficiency, fuel consumption, and so on. In addition, according to alternative embodiments, the present method may recommend specific actions, such as whether or not to take a drain credit, the gas turbine units that should be shut down, and / or those that should be shut down (for example, historical start-up reliability and potential imbalance loads that may occur due to a delayed start). The present invention may also be used to improve fuel consumption predictions such that future fuel purchases are made more precise or, alternatively, allow for fuel purchases for market periods that are more in the future that have a positive effect on fuel price and / or price to maintain a leaner fuel stock or a margin.
FIG. 19 illustrates an exemplary embodiment of a trim advisor according to an exemplary embodiment of the present invention, which is in the form of a flowchart 700. The adjustment advisor may be used to advise on the relative costs during a future period of operation of shutting down a power plant or portion thereof while operating other of the generating units in a shutdown mode. According to this exemplary embodiment, the potential costs associated with the shutdown and shutdown mode of operation may be analyzed and then communicated to a power plant operator for appropriate action.
As initial steps, certain data or operating parameters may be collected which may affect the operating costs or may be used to determine the operating costs during the selected trim operation period. These are grouped, as illustrated, into: Abregeldaten 701, Abschaltdaten 702 and common data 703. The common data 703 have those cost elements that affect both shutdown and Abregelbetriebsmodi. The common data 703, for example, indicates the selected operation time period for which the analysis of the Abregelbetriebsmodus is performed on. It should be understood that more than one selected operating period may be defined and analyzed separately for competing trim modes so that more extensive optimization is obtained during an extended timeframe. It will be understood that defining the selected operating time period may include defining the length of the time period as well as its start or end point. Other common data 703 may include, as shown, the fuel price, the various emission limits for the power plant, and data related to environmental conditions. As far as emission limits are concerned, the collected data limits may have been incurred during a defined regulatory period, such as one year, and the quantities already incurred by the power plant and the extent to which the applicable regulatory period has already been established , Furthermore, emissions data may include penalty payments or other costs associated with exceeding any of the limits. Thus, the present method can be informed of the current status of the power plant with respect to annual or periodic regulatory limits as well as the likelihood of possible infringement and penalties associated with such non-compliance. This information may be relevant to determining whether generation units are shut down or stalled because each type of operation has a different impact on power plant emissions. As for the environmental condition data, such data may be obtained and used in accordance with the processes already described herein.
It should be understood that the Abregelbetriebsmodus has data that are relevant only to a determination of the operating costs associated with it. Such off-regulation data 701, as illustrated, has revenue that can be earned from the power that is generated while the power plant is operating at the stall level. More specifically, there is the potential that the power will generate revenue for the power plant because the stall mode is one in which power generation continues. Insofar as this is done, the income can be used to compensate for some of the other operating costs associated with the Abregelbetriebsmodus. The present method thus includes receiving a price or any other economic indication associated with the sale or business use of the power that the power plant is producing while operating in the trim mode. This may be based on historical data, and the income earned may depend on the level of regulation with which the power plant operates.
The regulation data 701 may further include operation and maintenance associated with the operation of the power plant at the regulation level during the selected operation period. This may also be based on historical data, and such costs may depend on the level of regulation, for the power plant, and how the power plant is configured. In some cases, this load can be represented as hourly costs, which depend on a load level and historical records-like operation. The deceleration data 701 may further include data related to power plant emissions when operating in the Abregelmodus.
The shutdown data 702 also includes a plurality of elements unique to the shutdown mode of operation, and this data type may be collected at this stage of the current process. According to certain embodiments, this includes data associated with operation of the launch device during the shutdown period. Additionally, data related to different phases of the shutdown operation is defined. These may, for example, comprise data associated with: the shutdown process itself, which may include historical data on the length of time required to bring the generating units from a normal load level to a state in which the starting device is deployed is, with the length of time, while the power plant remains switched off according to the selected operating period, the length of time during which the generating unit typically remains on the starting device, and with data associated with the process by which the generating units after the shutdown new be started or reconnected to the grid, as well as the time required for it, starting fuel requirements and Anfahremissionsdaten. When determining the startup time, information about the possible startup types for the generation unit and specifications associated therewith can be determined. One skilled in the art will understand that start-up processes may depend on the time during which the power plant remains shut down. Another consideration that affects the startup time is whether the power plant has certain characteristics that affect or shorten the startup time and / or whether the operator of the power plant chooses to use any of these features. An emptying process may, for example, extend the start-up time if necessary. However, an emptying loan may be available if the power plant has been shut down in a certain way. Fixed costs associated with the shutdown, including those associated with startup, may be determined during this step, as well as costs inherent in any of the relevant generating units. Emission data associated with starting and / or stopping the power plant may also be determined. These may be based on historical records or otherwise. Finally, data associated with the starting reliability of each of the heat generating units can be determined. It will be appreciated that power plants may be charged fees, fines and / or penalties if the process of returning units to the network has delays resulting in the power plant being unable to meet its load obligations. These costs may be determined and, as discussed in more detail below, determined in light of the historical data associated with the start-up reliability. Such loads can thus be neglected to reflect the likelihood of their occurrence and / or to include an expense that will insure or insure the risk of such loads.
From the initial data acquisition steps of 701-703, the exemplary embodiment illustrated in FIG. 19 may be based on a trim analyzer 710 and a shutdown analyzer 719, each configured to provide operating cost for the operating mode to which it corresponds. to calculate. As illustrated, each of these analyzers 710, 719, for providing cost, emissions and / or other data, may proceed to step 730, where data associated with possible cancellation or unit shutdown scenarios is compiled and compared, such that Finally, an output to a power plant operator at step 731 can be made. As discussed, this issue may include costs and other considerations for one or more of the possible scenarios, and may eventually recommend a particular action and the reasons for it.
With regard to the trim analyzer 710, the method may first determine the load level for the proposed trim operation during the selected operating period. As discussed in more detail below, much of the cost associated with the trim control process may significantly depend on the load level the power plant is operating on, as well as how the power plant is configured to generate that load, which may include, for example, how the various heat generation units are used (that is, which are regulated and which are switched off). The Abregellastniveau for the proposed Abregelvorgang can be determined in various different ways according to alternative embodiments of the present invention. First, the power plant operator can select the cut-off load level. Then the load level can be selected on the basis of analytical histories related to past shutdown load levels at which the power plant has operated efficiently. Based on these records, the proposed load level may be analyzed and selected based on operator-supplied criteria, such as efficiency, emissions, meeting one or more site-specific objectives, availability of alternative commercial uses for the power generated during the stall condition, environmental conditions as well as other factors.
[0145] As a third method of selecting the level of adjustment for the proposed trim action, a computer-implemented optimizer, such as that described in connection with Figure 18, may be used to calculate an optimized trim level. This process is illustrated in FIG. 19 by steps 711 and 712. An optimized regulation level may be calculated by proposing Abregelbetriebsmodi in step 711 and then analyzed in step 712, if the operating limits for the power plant are met. It should be understood that a more detailed description of how this is accomplished is provided above in connection with FIG. Using a process such as this to optimize the level of regulation, it should be understood that the Abregelbetriebsmodi that are selected for comparison with the shutdown alternatives for the selected operating period, optimized cases represent, and that in view of the comparison between the Abregel- and The shutdown alternatives makes sense. As mentioned in connection with FIG. 18, the minimum regulation level can be calculated by means of an optimization process which optimizes the regulation level according to criteria selected by the operator. One of the functions may be the level of fuel consumption during the proposed settling period. The optimized regulation level can therefore be determined by optimizing fuel consumption to a minimum level, while also meeting all other operational limits or site-specific performance goals.
[0146] From this, the present method of FIG. 19 may determine the costs associated with the proposed Abregelbetriebsmodus for the selected operating period according to the Abregelbetriebsmodus characteristics, which were determined by the steps 711 and 712, respectively. As illustrated, step 713 may calculate the fuel consumption and therefrom the fuel cost for the proposed trim operation. According to the exemplary embodiment just discussed, which describes optimization based on minimizing fuel consumption, fuel costs may be derived by simply taking the calculated fuel level as part of the optimization step and then multiplying it by the expected or known price of fuel. At a next step (step 715), the income generated by power generated during the selected operating period may be calculated in view of the proposed level of regulation and the availability of commercial demand during the selected operating period. Then, at step 716, operating and maintenance costs may be determined. The operating and maintenance costs associated with the proposed trim mode may be calculated by any conventional method and may depend on the level of regulation. The operating and maintenance costs may be represented as an hourly load derived from historical records of stall operations, and may include a component utilization load representing a fraction of the expected life of different system components used during the proposed stall operation. At a next step, indicated by step 717, net costs for the proposed Abregelbetriebsmodus for the selected operating period can be calculated by adding the costs (fuel, operation and maintenance) and deducting the income.
The present method may also include step 718 that determines the power plant emissions during the selected operating time period in view of the proposed trim mode of operation, which may be called the "emission impact". The net cost and emissions impact may then be provided to a compile and a compare step illustrated as step 730 so that the cost and emissions impact of different default scenarios may be analyzed such that a recommendation is made at issue 731, as described in more detail below , can be provided.
With reference to the shutdown analyzer 719, it may be used to calculate aspects associated with the operation of one or more of the generating units of the power plant in a shutdown mode of operation during the selected operating period. As part of this aspect of the invention, operations having procedures for shutting down the power plant and then restarting at the end of the selected time period may be analyzed for cost and emissions. In accordance with a preferred embodiment, shutdown analyzer 719, as part of initial steps 720 and 721, may determine a suggested shutdown mode of operation that may represent an optimized shutdown mode of operation. The proposed shutdown mode of operation includes processes whereby one or more of the generating units are shut down and then restarted to reconnect the units to the grid at the end of the selected operating period. It should be understood that the length of time that a generating unit is not operating determines the type of possible starting processes that are available to it. Whether a hot or cold start is available depends on whether the shutdown period is short or long. In determining the proposed shutdown mode of operation, the present method may calculate the time required for the startup process to return the generating unit to an operating load level. At step 721, the method of the present invention may check to ensure that the proposed shutdown operation procedure meets all operational limits of the power plant. If one of the operating limits is not met, the method may return to step 720 to calculate an alternative start-up procedure. This can be repeated until an optimized start-up procedure that meets the operational limits of the power plant is calculated. It will be understood that according to the methods and systems discussed above, the tuned power plant model may be used to simulate alternative shutdown modes of operation to determine optimized cases in view of operating time and projected environmental conditions.
[0149] Given the proposed shutdown mode of operation of steps 720 and 721, the process may continue to determine the associated cost. The initial steps include analyzing the nature of the startup process that the shutdown mode of operation has. At step 722, the process may determine the specific operating parameters of the startup, which may include determining whether drainage is required or required by a power plant operator. Fuel cost may be determined at step 723 in view of the particular startup. According to an exemplary embodiment, the shutdown analyzer 719 then calculates costs associated with the delays that sometimes occur during the startup process. In particular, as indicated in step 724, the process may calculate the likelihood of such a delay. This calculation may include as input the type of start-up as well as historical records associated with past start-ups of the respective generating units in the power plant as well as data related to start-ups of such generating units in other power plants. As part of this, the process may charge for costs associated with the proposed shutdown mode of operation reflecting the likelihood of a startup delay occurring, and penalties, such as penalties, that may be incurred. These costs may include any costs associated with a hedging policy by which the power plant passes on part of the risk of such penalty payments to a service provider or other insurer.
At step 726, the present method may determine costs associated with the operation of the launch device during the shutdown process. The method may calculate a speed profile for the launch device in view of the shutdown period and, using it, determines auxiliary service costs required to operate the starting device. It should be understood that this is the power required to rotate the rotor blades of the gas turbine as it cools, which is done to avoid warping or deformation that would otherwise occur if the blades are allowed to to cool in stationary position. At step 727, as illustrated, operating and maintenance costs for the shutdown operation may be determined. The operating and maintenance costs associated with the proposed shutdown may be calculated by any conventional method. The operating and maintenance costs may include a component utilization load representing a fraction of the expected life of different system components used during the proposed shutdown operation. At a next step, indicated by step 728, net cost for the proposed shutdown mode of operation for the selected operating period may be calculated by adding the cost of fuel, starting device, and operation and maintenance. The present method may also include step 729 of determining power plant emissions during the selected operating time period in view of the proposed shutdown operating mode, which may be referred to above as the "emission influence" of the operating mode. The net cost and emissions impact may then be provided to the compilation and comparison step.
At step 730, the current method may compile and compare different power plant trim modes for the selected operating time period. According to one embodiment, the current method may analyze competing Abregelbetriebsmodi identified as part of the methods and processes described in connection with Figs. 18 to 20. At step 730, the compiled cost data and the emission impact for each of the competing trim modes may be compared and provided as an output as part of step 730. Thus, depending on how the competing modes of operation compare, a recommendation may be made as to how the plant should be operated during the selected shutdown period, including which of the turbines should be shut down and which should be shut down, and the stall level at which they operate should.
Emission data may also be provided as part of the output of step 731, especially in cases where the analyzed competing modes of operation have similar economic results. It should be understood that disclosure of the effects of each alternative on the power plant emissions, and given the impact, the likelihood of non-compliance during the present regulatory period, may also be provided, as well as an associated economic result. In particular, the summed emissions of one or more power plant pollutants may be compared during the regulatory period with overall limits permitted during that time frame. According to certain preferred embodiments, the step of communicating the result of the comparison may include indicating an emission rate of the power plant that is averaged by a cumulative emission level for the power plant during a portion of an ongoing regulatory emission period associated with an emission rate Emission limit is calculated during the ongoing regulatory emission period. This can be done to determine how the power plant behaves when compared to average emission rates that are allowed without risking a violation. The method may determine the emissions that are still available to the power plant during the current regulatory period and whether sufficient levels are available to either account for the proposed operating modes, or instead, if the emissions are inadmissible, increases the likelihood of future regulatory infringement.
As an issue, the present method may provide a recommended action that advises between the proposed turn-off and turn-off modes of operation in the context of the advantages / disadvantages, whether economic or otherwise. The recommendation may include cost reporting and a detailed breakdown of the categories in which these costs were incurred and the assumptions made in their calculation. In addition, the recommended action may include an overview of any other considerations that might affect the decision that selects the most favorable operating mode. These may include information related to applicable emission limits and regulatory periods, as well as how the ongoing cumulative emissions of the power plant behave in comparison. This may include informing operators of power plants of any mode of operation that unreasonably increases the risk of emissions threshold breaches and the costs associated with such violations.
The present invention may further include a unified system architecture or integrated computational control system that efficiently enables and improves the performance of many of the functional aspects described above. Power plants, including joint ventures, often operate across different markets, state jurisdictions and time zones, and have many types of stakeholders and decision-makers involved in their administration, and exist under different maintenance and other contractual arrangements. Within such diverse circumstances, a single owner may control and operate a number of power plants, each having multiple generating units and types, over overlapping markets. Owners may also have different criteria for assessing effective power plant operation, including, for example, clear cost models, response times, availability, adaptability, cybersecurity, functionality, and differences of how separate markets work. However, it is understood that most current power markets are based on different off-line files shared by multiple parties and decision makers, including those transferred between traders, power plant managers, and regulators. In view of such complexities, the capabilities of power plants and / or generating units within a market segment can not be fully understood, particularly over the layered hierarchy, for example, from individual generating units to power plants or from power plants to fleets of such power plants. Each successive level of the power trading market typically hedges the power reported from the underlying level. This results in inefficiencies and income losses for the owners while compounding successive hedges into systemic underutilization. Another aspect of the present invention, as discussed below, works to alleviate shutdowns that are not the root of these problems. According to one embodiment, a system or platform is developed that performs analytics, gathers and evaluates historical data, and performs what-if or alternative scenario analyzes on a unified system architecture. The unified architecture can more efficiently enable various functions, various components, such as power plant modeling, business decision support tools, power plant operation prediction and performance, and optimization according to performance objectives. In certain aspects, the unified architecture may achieve this by integrating local components of the power plant with those remote from it, such as those located on a centrally hosted or cloud-based infrastructure. It is understood that aspects of such integration may allow for improved and more accurate power plant models while not affecting the consistency, effectiveness, or timeliness of the results. This may include using the previously discussed tuned power plant models on local and externally hosted computing systems. Given this distribution to an externally hosted infrastructure, the system architecture can be virtually customized to handle additional sites and units.
With reference to Figs. 22-25, scalable architectures and control systems are presented that may be used to support the many requirements associated with controlling, managing, and optimizing a fleet of power plants in which multiple generating units several places are scattered, are connected. A local / remote hybrid architecture, as provided herein, may be used based on certain criteria or parameters specific to the situation or case. For example, an owner or operator having a number of power plants may want to have certain aspects of the functionality of the system locally hosted while others are centrally hosted, such as in a cloud-based infrastructure, to collect data from all the generating units and act as a common set of data that can be used to manipulate cross-reference data of common equipment, configurations, and conditions while also supporting analytical functions. The method of selecting the appropriate architecture for each of the different types of owner / operator may focus on the significant concerns underlying the operation of the power plants as well as the specific features of the electricity market in which the power plants operate. According to certain embodiments, as provided below, power calculations may be performed locally to assist closed loop control of a particular power plant, improve cyber security, or provide the response speed required to accommodate near real time processing. On the other hand, the present system may be configured such that the data flow between local and remote systems includes local data and model tuning parameters that are transmitted to the centrally hosted infrastructure for creating a tuned power plant model, then for analytics, such as analysis of an alternative scenario , is used. Remote or centrally hosted infrastructures can be used to tailor interactions between a common power plant model according to the unique needs of the different types of users requesting access. In addition, a scaling strategy based on response time and service agreements that depend on the unique aspects of the particular market may be determined. If faster response times to the availability of final results are required, the analytical processes can be scaled in terms of both software and hardware resources. The system architecture also supports redundancy. If any system performing analytics fails, processing may continue on a redundant node having the same power plant models and historical data. The unified architecture can bring together applications and processes to enhance performance and expand functionality to achieve both technical and commercial benefits. It is understood that such advantages include the practical integration of new power plant models, separation of procedures and models, enabling the sharing of the same data in real time between different operators while also presenting the data in unique ways according to the needs of each operator Retrofits and compliance with NERC CIP limits for sending monitoring controls.
FIG. 22 illustrates a high level logic flowchart or fleet level optimization method in accordance with certain aspects of the present invention. As shown, the fleet may include multiple generating units or stocks 802 that may represent separate generating units in multiple power plants or the multiple power plants themselves. The stock 802 of the fleet may be owned by a single owner or unit and may compete with other such stocks in one or more contract rights markets for generating portions of the load needed by a customer network. The stocks 802 may include multiple generating units having the same type of configurations. At step 803, performance data collected by the sensors at the different inventories of power plants may be electronically communicated to a central repository. Then, the measured data may be adjusted or filtered at step 804 so that, as described below, a more accurate or accurate indication of the power level for each inventory is determined.
As described in detail above, one way of performing this comparison is to compare the measured data with corresponding data predicted by the power plant models that, as discussed, may be configured to simulate the operation of one of the inventories. Such models, which may be called off-line or predictive models, may have models based on physics, and the adjustment process may be used to tune the models at regular intervals to determine the precision with which the models actually operate based on simulation. maintain and / or improve. As discussed in detail above, therefore, at step 805, the method may use the most recent collected data to tune the power plant models. This process may include reconciling the models for each of the inventories, that is, for each of the generating units and / or each of the power plants, as well as for further generalized models covering the operation of multiple power plants or aspects of fleet operation. The matching process may also involve comparing the collected data with similar stocks 802 to resolve discrepancies and / or identify anomalies, particularly data collected from the same type of inventory having similar configurations. During this process, gross errors can be eliminated given the common and redundant nature of the compiled data. For example, sensors that have higher precision capabilities than those known to have recently been tested and proven to work correctly can be considered. Thus, the collected data can be cross-checked, verified, and reconciled to build a single consistent record that can be used to more accurately calculate actual fleet performance. This data set can then be used to tune offline inventory models, which can then be used to determine optimized fleet control solutions during a future market period, which can be used, for example, to improve power plant competitiveness during allotment bidding procedures.
At step 806, as illustrated, the actual capabilities of the power plant are determined based on the adjusted performance data and matched models of step 805. Then, the stocks 802 of the fleet may be optimized together at step 807 in consideration of a selected optimization criterion. It should be understood that this may involve the same processes as those already discussed in detail above. At step 808, an optimized supply curve or inventory planning may be generated. This may describe the way in which the stocks are planned or operated and the level at which they are deployed, for example to meet proposed or hypothetical load levels for the fleet of power plants. The criteria for optimization may be selected by the operator or owner of the stocks. The optimization criteria may include, for example, efficiency, income, profitability, or any other measurement.
As illustrated, subsequent steps may include communicating the optimized inventory planning as part of a lottery contract bid for future market periods. This may include communicating the optimized inventory planning to energy traders at step 809, which then bid according to optimized inventory planning. It is understood that the offers may be used at step 810 to participate in an allocation process that extends across the power system, where the load is distributed among multiple power plants and generating units located within the system, one of which Many can be owned by competing owners. The bids or quotes for the allocation process may be configured according to a defined criterion, such as variable generation costs or efficiency, as defined by the particular allocator of the power system. At step 811, the power system optimization results may be used to generate inventory planning that reflects how the various inventories in the power system should be deployed to meet the predicted demand. The inventory planning of step 811, which reflects the result of the cross-system optimization or allocation process, may then be communicated back to the owners of the stocks 802 such that at operation 812, operating setpoints (or, in particular, operating models) having, for example, the load, with the each of the inventories is operated, can be communicated to a controller that controls the operation of the stocks 802. At step 813, the controller may calculate a control solution and then communicate and / or directly control the inventories 802 to meet the load requirements that it has committed to during the allotment process. Fleet owners can adjust the way one or more power plants work as conditions change to maximize profitability.
Fig. 23 illustrates the data flow between local and remote systems according to an alternative embodiment. As noted, some functionality may be hosted locally while other functionality is hosted off-site in a centrally-hosted environment. The method of selecting the appropriate architecture according to the present invention includes determining the considerations that are significant factors for the operation of the inventory within the fleet. Considerations, such as cybersecurity concerns, may thus require certain systems to remain local. Time-consuming performance calculations also remain locally hosted so that the required timeliness is maintained. As illustrated in FIG. 23, a local power plant control system 816 may receive sensor measurements and communicate the data to a tuning module 817 where, as discussed above in connection with FIG. 17, a tuning or balancing process using power calculations, current or measured Values can be performed with those predicted by the power plant or inventory model. Using the data router 830, the model tuning parameters and adjusted data may then be communicated as illustrated to a centrally hosted infrastructure, such as the remote central database 819. Based on this, the model tuning parameters are used to tune the offline power plant model 820, which can then be used as described above to optimize future fleet operation, provide analysis of an alternative scenario or "las-if" analysis, and between possible or competing ones To advise on operating modes of the existing fleet.
The results of the analyzes performed using off-line power plant model 820 can be communicated to fleet operators via web portal 821, as illustrated. Web portal 821 may provide users with custom access 822 for managing the fleet. Such users may include the power plant operators, energy traders, owners, fleet operators, engineers, and other stakeholders. According to the user's exchange score through the web portal access, decisions may be made in the context of the recommendations offered by the analytics using the offline 820 power plant model.
Figures 24 and 25 illustrate schematic system configurations of a unified architecture according to certain alternative aspects of the present invention. As illustrated in FIG. 25, a central inventory and analytics component 825 may receive performance and measured operating parameters from multiple inventories 802 to perform fleet level optimization. The fleet level option may be based on additional input data including, for example, the actual fuel quantities stored and available at each power plant, the site-specific price of fuel for each power plant, the site-specific price for the power generated in each power plant, current weather forecasts and dissimilarities between remote stocks, and / or outage and maintenance schedules. For example, a planned component overhaul for a gas turbine may mean that short-term operation at higher temperatures is more economical. The process can then calculate a supply curve that has an optimized variable that generates costs for the fleet of power plants. In addition, as illustrated, the present invention may further facilitate automated bid preparation so that, at least under certain circumstances, the bid may be transmitted directly to the system-wide arbitration authority 826, thereby bypassing the energy traders 809. As illustrated in FIG. 25, the power system optimization results (using the system-wide arbitration authority) may be used to generate inventory planning that reflects how the various inventories in the power system should be deployed to meet the predicted demand. This inventory planning may reflect cross-system optimization and, as illustrated, be communicated back to the owners of the inventory fleet 802 so that plant setpoints and operating modes may be communicated to the inventory of the controller controlling each inventory in the system.
The methods and systems may thus be developed in accordance with Figures 22 through 25, which optimizes a fleet of power plants operating within a competitive power system to improved performance and bidding associated with future market time periods. Actual data associated with operating conditions and parameters can be received in real time from each of the power plants within the fleet. The power plant and / or fleet models can then be tuned according to the actual data such that the model precision and the prediction range are further improved. It is understood that this can be achieved by comparing measured performance indicators and corresponding values predicted by the power plant or fleet models. As a next step, the tuned power plant models and / or fleet level models may be used to calculate actual generating capabilities for each of the power plants within the fleet based on competing operating modes simulated with the tuned models. An optimization then takes place using the actual power plant capabilities and optimization criteria defined by the power plant or fleet operator. In determining an optimized operating mode, inventory planning may be generated that calculates optimal operating points for each of the power plants within the fleet. It will be appreciated that the operating points may then be transferred to the various power plants to check their compliance therewith, or alternatively the operating points may serve as the basis upon which bids are submitted for submission to the central dispatching authority.
It is understood that the economic and performance optimization processes discussed herein, at least according to certain embodiments, depend on a tuned power plant model that maps or simulates different types of power plant operation. When implemented successfully, such power plant models can be used to analyze alternative scenarios to determine more efficient operating modes that otherwise might not have been recognized. A necessary component in building the sophisticated power plant models required is the availability of high-precision data that measures power plant operating and performance parameters during operation. Further, after construction, the process of maintaining and recalibrating such power plant models requires ongoing input of reliable data, because a previously tuned power plant that worked well may quickly decline if data deemed accurate, but which instead turns out to be deficient , are entered. A major consideration remains the proper functioning of the many sensor types used to measure and communicate power plant conditions and performance parameters during operation. The rapid identification of sensors that fail or do not work properly is thus an important component of the optimization and control systems described above. Otherwise, large amounts of otherwise reliable data may be corrupted by poor readings by a single sensor that are unrecognized. Poor data may also have a downstream effect that increases its negative impact in that, as the poor data is used to tune power plant models, the models no longer reflect current power plant operation and therefore make control recommendations that reflect ineligible or efficient operating modes.
[0165] In accordance with the multiple embodiments illustrated in FIGS. 26-31, an additional aspect of the present invention is discussed that relates to a multi-step approach to assessing the operation of power plant sensors by analyzing the data that the sensors record. It should be understood that unless otherwise specifically limited to a more specific case, the method described herein for testing the proper functioning of a sensor or group of sensors (also called "sensor health check") for any of the types of sensors already discussed , as well as for another sensor type of gas turbines and / or similar devices. As will be described, the present method may include reviewing and assessing data in real-time as it is collected, as well as judging after communicating and cataloging the data measurements at a remote or off-site storage system, such as a central or cloud-hosted dataset , The evaluation of the sensors and data collected by them can be configured to incrementally repeat within a specified time interval to provide a time-based and evolving view of sensor performance. Further, as described, the present method may include real-time sensor performance or failure data evaluations, such as displacement, drift, senility, spike noise, etc., as well as less frequently performed evaluations that focus on data acquired during a longer period of time Power plant operating time were collected. According to certain embodiments, the process may detect sensor failures by comparing measured sensor values to predicted values modeled by tuned power plant models. These embodiments reflect the discovery that combining certain types of real-time data analysis that occurs during a shorter review period with certain other analyzes that have a longer review period for accurately and quickly identifying sensors that are not functioning properly is particularly effective and accurate is. As envisioned herein, a "look-back period" is the power plant operating time period for which the data signals from the sensor or sensors are analyzed for certain types of irregularities that indicate sensor failure or increased risk for it. As used herein, a "short review period" is one that collects sensor readings and / or the data from such readings for operation that occurs within a few minutes past, for example, for the last 5 minutes, although other similar durations are also possible , A "long review period" is defined as one that collects sensor readings and / or the data from such readings within a few hours past. According to a preferred embodiment, a long review period is one that has a duration of about 1.5 hours. As described in greater detail below, the long review period may be divided into a plurality of regularly spaced intervals. According to exemplary embodiments, the intervals of the long review period may be configured to match the lengths of the short review period. In such cases, according to a preferred embodiment, the number of intervals included in the long review period may be about 10 to 20. It should be understood that in view of this arrangement, the short review period may be at least one of the intervals that make up the long review period.
FIG. 26 illustrates a schematic process diagram of a method 850 according to an embodiment of the present invention. It is understood that several types of sensor health checks are included within method 850 and depicted as working together as components of the overall approach. However, it should be understood that this has been done for purposes of brevity of description and therefore description of an exemplary embodiment. As outlined in the appended claims, several types of sensor health exams (or "exams") may work individually or in different combinations as those provided in FIG. 26.
At an initial step, the method 850 includes a node 851 that initiates a gradual or incremental loop through which multiple types of sensor groups are sequentially analyzed. According to a preferred embodiment, data from sensor readings in data sets can be read out and collected in 5 minutes. In accordance, health checks may be configured to sample or analyze the most recent 5-minute data set, or may be configured to analyze data from several of the most recent recorded data sets, as indicated in the description of each of the various types of sensor health exams. The sensor data may be sent by test utilities, which, as illustrated, are contained within a second loop defined within the first one. In particular, at a node 853, the second loop may function to trim incremental of each of the sensors of the sensor array by a number of different health checks, which as illustrated may include a continuity check 854, a data check 855, a model check 856, and an area check 857. Once these health checks for one of the sensors in the group are completed, the process returns to node 853 until it is determined that there are no more sensors within the sensor group. At this time, as illustrated, method 850 proceeds from node 853 to an additional health check, which is an averaging check 859, before returning to node 851, which marks completion of the first loop. The method may continue to unwind the first loop until it is determined that all sensor groups have been tested. As described in more detail below, the sensor readings may be tagged in the course of completing the health checks to indicate concerns with the data and thereby the sensors that recorded the data. The accumulation of multiple tagged readings within the data set of a particular sensor may be used as an indication that the sensor is disturbed or at least more likely to be disturbed.
Once all sensors of the sensor groups have been treated, the method may proceed to an output step 861. As part of the output step 861, the method 850 may electronically communicate one or more results in view of the health checks that have been performed. Such communication may take the form of, for example, an e-mail or a screen alert for a power plant operator or personnel. In such cases, the output may be configured to contain different information and / or be formatted according to predetermined alert categories, such as a more serious alert indicating a high probability that one of the sensors is disturbed in view of the analyzed sensor readings, or a less serious warning that communicates questionable readings. The severity of the alert may, according to a preferred embodiment, depend on the number of times that the record has been tagged by the multiple health checks. The output may also include indications from sensors that have been judged to work correctly. In these cases, the output may provide a report of the health checks that have been performed, data associated with the analysis, as well as an explanation of why the sensors are functioning and considered normal. According to another embodiment, the output may comprise automatic steps that are taken when the results of the health checks have described certain predefined situations. For example, in the case where a sensor is shown as being disturbed, the use of the data collected by that sensor may be interrupted until the problem has been addressed. The issue of the health exams can be stored in a central repository or historian for later viewing of the results and their change over time. According to an alternative embodiment (not shown), the present invention may include a step of determining whether the analysis results from the sensor health checks correspond to an explanatory event, which may be, for example, a change in the operation mode of the gas turbine. In particular, the method may determine whether the tagged sensor readings may be explained or consistent with a concurrent and intended operational change for the machine, such as a change in the output level. If this is likely to happen, additional actions may be taken to confirm that the displacement measured by the sensors matches the change in operation.
[0169] Referring to FIGS. 27-31, the functionality of the multiple health checks will be discussed in connection with exemplary embodiments. The continuity check (represented by step 854 of FIG. 26) may operate in accordance with approach 870 of FIG. 27. As illustrated, in an initial step 871, sensor readings may be collected during a predetermined lookback period. According to an exemplary embodiment, the look back period may be about 5 minutes. An initial continuity health check may include determining whether at least a minimum number of readings have been taken during the review period. The readings from the sensor should therefore have at least a minimum number of readings or data points during the predefined look-back period. As illustrated by node 872, approach 870 may determine whether the number of readback readings is sufficient. More specifically, the total number of readings for the look-back period may be compared to a predetermined acceptable minimum. If the sum of the readings is less than the required minimum, the sensor can be marked. On the other hand, if the sum of the readings is greater than the required minimum, that part of the continuity test may be considered passed, and the method may proceed to step 874, where a portion of unavailable readings within the sum of the readings is determined. Unavailable readings represent those where the sensor is operating and readings are scheduled, but the data is either unavailable, inaccurate, and / or otherwise unexplained. In determining the unavailable readings, the procedure may determine the percentage that the unavailable readings make to the sum of the readback period readings. At the following step, as indicated by node 875, the percentage of unavailable readings may be compared to a predetermined maximum threshold. If the percentage of unavailable readings is greater than the threshold, the process may proceed to step 876 where the sensor is highlighted. However, if the percentage of unavailable readings is less than the predefined maximum threshold, the process may proceed to step 877, which is a completion of the continuity health check. At this stage, the approach 870 may proceed to the next sensor within the group and perform the same test, or, if all the sensors in the group have already been tested, the procedure for the next health check within the overall procedure may proceed.
According to an exemplary embodiment, the data validation (represented by step 855 of FIG. 26) may operate in accordance with approach 880 of FIG. 28. As illustrated, at an initial step 881, sensor readings may be collected during a predetermined lookback period. According to a preferred embodiment, this look back period may be about 5 minutes. The sensor readings can then be checked sequentially for different types of data irregularities. For example, at node 882, the procedure may determine whether a shift is indicated in view of the sensor readings during the review period, as illustrated in exemplary data plotter representation 883. It should be understood that, for want of other causes, data that indicates a noticeable or otherwise unexplained shift of this type often indicates a problem with the sensor rather than an actual shift in the operating parameters being measured. If a shift is deemed to have occurred, the process may mark the sensor as illustrated and then proceed to the next test. At node 884, the procedure may determine whether high speeds are indicated by the sensor readings, an example of which is illustrated in the example data plotter representation 885 provided. This type of data plotter representation may also indicate a problem with the sensor. If the record meets the criteria to find that high-speed data occurs, the process marks the sensor as indicated. If the high-speed behavior is not noticed, the procedure may proceed to the next node 886 where the procedure checks the record to determine if a data drift irregularity is specified. A data drift can also indicate a sensor fault. As illustrated in the exemplary data plotter representation 887, drift irregularity occurs when the data values inexplicably deviate from what would otherwise be expected based on historical readings. It should be understood that this type of irregularity is similar to the data shift but, as illustrated, occurs more gradually. If the data meets the definition of drift irregularity, the process can mark the sensor as indicated. As a final test, the process may determine if noise or interference data irregularities are present in the data set. As illustrated in the exemplary plotter illustrations, these may include instances where random noise increases significantly above previous levels, as shown in the data plotter representation 889, cases where random noise decreases substantially, as shown in the data plotter plot 890, and cases where in the case of senility, as shown in the exemplary data plotter representation 891, it is noted that the readings substantially cease altogether. The data checks therefore comprise determining whether a sequential plot of the readings of a data set during the review period produces a profile indicative of data irregularity. If any of these irregularities are indicated, the sensor can be marked. At this stage, the data review procedure 880 may end at step 891 where another of the health checks may be initiated in accordance with the method of FIG. 26.
In accordance with an exemplary embodiment, the model check (represented by step 856 of FIG. 26) may operate in accordance with the procedure 900 of FIG. 29. As part of this particular health check, data collected by the sensors is compared to corresponding values predicted by a tuned power plant or generation unit model to determine if there is a disparity between the two that changes over time , According to a preferred embodiment, the model may be a physics-based thermal model for either one of the generating units or the plant as a whole. As illustrated, at an initial step 901, sensor readings may be collected during a predetermined lookback period. At the same time, a tuned model may be used at step 902 to predict sensor readings corresponding to the current readings taken by the sensors during the review period. It will be understood that the power plant or generation unit model may be tuned and used in accordance with any of the approaches discussed in detail herein. At step 903, a comparison may be made between the predicted values and the values measured by the sensors. According to a more superficial first test, sensors may be marked at step 904 based on this first comparison. This determination may simply be based on whether the differences between the predicted and measured values are significant enough to create concern for the reliability of the measured values. A second comparison may be made at step 905. According to this check, the procedure may compare the comparison between the predicted and measured values of the current look-back period with the same comparison made during a previous look-back period. As part of this, this approach may define a second look-back period that is significantly longer than the shorter 5-minute period. For example, in a preferred embodiment, the second look-back period may be about 1.5 hours. As part of the analysis, the approach according to a preferred embodiment may compare how the patterns look between the most recent comparisons between current / predicted sensor values compared to the comparison performed earlier in the second lookback period. This process may proceed to step 906 where patterns in the comparisons of the current / predicted sensor values are evaluated to determine how the patterns change during each of the increments to 5 minutes over the longer review period. In fact, the comparison between the predicted / measured readings made for each of the short look-back periods, for example, the 5-minute look-back period, may be examined in comparison to one another to determine how the relationship between the predicted / measured Values during the longer review period, for example the 1.5-hour review period. It is understood that certain changes in this relationship may be used during the longer review period to indicate situations in which one of the sensors is disturbed or likely to be disturbed. In cases where the patterns indicate such a changing relationship, the sensor may be marked at step 901. Based on this, the procedure may proceed to step 909 to end this particular health check.
[0172] In accordance with an exemplary embodiment, the range check (represented by step 857 of FIG. 26) may operate in accordance with approach 920 of FIG. 30. According to a preferred embodiment, the look-back period may be relatively short, for example in about 5 minutes. This variation of the sensor health check includes determining if the data readings fall within an expected predetermined range. At an initial step 921, sensor readings may be collected for the review period. Then, the approach at node 922 may initiate a loop through which each data point is then tested. In particular, at node 923, each of the data points is tested to determine if the data point is greater than a predefined maximum or less than a predefined minimum. It should be understood that the predefined maximum and minimum may be an area defined by an operator and / or defined in association with historical readings based on past operation and configured to represent a ceiling and floor through which no Matching or different data points are detected. According to preferred embodiments, the maximum and minimum thresholds may be configured as values having a low probability of occurrence during a given mode of operation. As illustrated, if the data point is exceeded as the predetermined maximum or below the predetermined minimum, the sensor responsible for the data point may be marked at step 924. Once each of the data points has been tested within the dataset of the review period, the procedure may proceed to step 925 where that particular health check ends.
According to an exemplary embodiment, the averaging check (represented by step 859 of FIG. 26) may operate in accordance with approach 930 of FIG. 31. In this variation, each of the sensors is tested against an area defined by an average of the readings from all the sensors within the group. As illustrated, the procedure may begin with collecting the readings from the sensors within the group during the review period. According to a preferred embodiment, the review period for this health check may be 5 minutes. As illustrated in Figure 26, the averaging test 859 is a test that is applied to the sensor group as a whole, unlike the other health checks shown separately applied to each sensor. At step 933 of averaging procedure 930, the approach, as illustrated, may calculate the average for a particular operating parameter in consideration of the readings taken by the sensors within the sensor array. At node 934, the approach may initiate a loop through which each data point is then tested according to an area defined around the calculated average. More specifically, at node 935, each of the data points is tested to determine if 1) it is greater than a predefined upper bound defined with the computed average of the sensor set, or 2) if less than a predefined lower bound Limit defined with the calculated average of the sensor group. It is understood that the predefined upper and lower limits may be configured to represent a relative range from which mismatched or differing data points are identified. As illustrated, if the data point is found to exceed the upper bound or lower than the lower bound, the sensor responsible for the data point may be marked at step 936. Once each of the data points within the dataset of the review period has been tested, the procedure may proceed to step 937, where it ends.
Although the invention has been described in conjunction with what is presently considered to be the more practical and preferred embodiment, it is to be understood that the invention is not limited to the disclosed embodiment but, on the contrary, is intended to disclose various changes and modifications equivalent arrangements included within the spirit and scope of the appended claims.

权利要求:
Claims (20)
[1]
A method of operating a sensor in a heat generating unit, wherein the sensor is communicatively connected to a control system and configured to take readings to measure an operating parameter related to operation of the heat generating unit, the method comprising the steps of:Defining review periods, where theReview periods each comprise previous operating periods for the heat generating unit, wherein theLook back periods have at least a first look back period and a second look back period,Receiving a first data set relating to readings for the sensor during the first look back period,Receiving a second data set relating to readings for the sensor during the second lookback period,Perform a first check on the first record and get a first result from it,Perform a second check on the second record and obtain a second result from it, andDetermining a probability of whether the sensor is disturbed based on the first and second results.
[2]
2. The method of claim 1, wherein the first review period includes a short review period, and the second review period includes a long review period, the second review period being several times longer than the first review period.
[3]
3. The method of claim 1, wherein the first review period comprises a short review period of approximately several minutes, and the second review period comprises a long review period of approximately several hours.
[4]
4. The method of claim 3, wherein the first look back period comprises about 1 to 10 minutes and the second lookback period comprises about 1 to 3 hours.
[5]
5. The method of claim 1, wherein the second look-back period comprises a plurality of regularly spaced intervals, each of the intervals being approximately the same length as the first look-back period, andwherein the first look-back period comprises at least one of the intervals of the second look-back period.
[6]
6. The method of claim 5, wherein a number of intervals included in the second lookback period comprises between about 10 and 20.
[7]
7. The method of claim 5, wherein the second test comprises a model test comprising the steps of:Calculating predicted values corresponding to measured values of the readings of the second data set, andComparing the predicted values with corresponding measured values from the second data set,wherein the predicted values are derived from a simulation of the operation of the heat generating unit.
[8]
8. The method of claim 7, wherein the first test comprises a continuity test comprising the steps of:Determining whether a total number of readings contained in the first data set is greater than a minimum allowable threshold, andDetermining a percentage of the total number of readings that includes unavailable readings, and then determining whether the percentage is less than a maximum allowable threshold.
[9]
9. The method of claim 7, wherein the first test comprises an area check comprising the steps of:Defining a range between a maximum threshold and a minimum threshold, wherein the range is based on values of historical readings of the sensor,Determining whether the readings included in the first record include values within the defined range.
[10]
10. The method of claim 7, wherein the first test comprises an averaging test comprising the steps of:Calculating average values for the readings in the first data set, the average value comprising averaging corresponding readings from the sensor and at least one other transmitter of the same type,Defining an area around the calculated oneAverage values, in which: a positive offset from the calculated average values comprises a maximum threshold value and a negative offset from the calculated average values comprises a minimum threshold value,Determining whether the readings included in the first record include values within the defined range.
[11]
11. The method of claim 7, wherein the first test comprises a data check comprising determining whether a sequential plot of the readings of the first data set during the first lookback period comprises a profile indicative of data irregularity.
[12]
The method of claim 11, wherein the data irregularity comprises the profile showing a data offset in the sequential plot of the readings.
[13]
13. The method of claim 11, wherein the data irregularity comprises the profile showing a data drift in the sequential plot of the readings.
[14]
14. The method of claim 11, wherein the data irregularity comprises the profile showing a data peak in the sequential plot of the readings.
[15]
The method of claim 11, wherein the data irregularity comprises the profile exhibiting at least one of increasing noise, decreasing noise, and senility in the sequential plot of the readings.
[16]
16. The method of claim 7, wherein the first test comprises a continuity test comprising the steps of: determining whether a total number of readings contained in the first data set is greater than a minimum allowable threshold and determining a percentage of the total Number of readings that includes unavailable readings, and then determining whether the percentage is less than a legal maximum threshold,wherein the first test comprises an area check comprising the steps of: defining a first range between a maximum threshold and a minimum threshold, wherein the first range is based on values of historical readings of the sensor, and determining whether the readings included in the first data set include values within the first range,wherein the first test comprises an averaging test, comprising the steps of: calculating averages for the readings in the first data set, the average value comprising readings corresponding to the sensor and at least one other sensor of the same type, defining a second range around the calculated one Average values in which a positive offset from the calculated average values includes a maximum threshold, and a negative offset from the calculated average values includes a minimum threshold, and determining whether the readings included in the first set include values within the second range and wherein the first test comprises a data check comprising determining whether a sequential plot of the readings of the first data set during the first lookback period comprises a profile indicative of data irregularity, m at least one drift, one shift and one peak.
[17]
17. The method of claim 7, wherein simulating the operation of the heat generation unit comprises a tuned model of the heat generation unit,further comprising the following steps:Acquiring and collecting measured values for a plurality of the operating parameters of the heat generating unit, andTuning a model of the heat generating unit to configure the tuned model of the heat generating asset, the tuning including a data matching process in which the measured values for selected parameters of the operating parameters are compared to predicted values for the selected parameters of the operating parameters to make a difference between them, on which the tuning of the model is based.
[18]
18. The method of claim 17, wherein the model of the heat generation unit comprises a physics-based model, and the tuned model of the heat generation unit comprises a tuned physics-based model.
[19]
19. The method of claim 5, wherein the second test comprises a model test comprising the steps of:Calculating predicted values corresponding to measured values from the second data set,Determining a relationship between the predicted values and the measured values within each of the intervals,Comparing the relationship between the predicted values and the measured values for a development pattern as the intervals progress through the second look-back period.
[20]
20. The method of claim 19, wherein simulating the operation of the heat generation unit comprises a tuned model of the heat generation unit,further comprising the following steps:Acquiring and collecting measured values for a plurality of the operating parameters of the heat generating unit, andTuning a model of the heat generation unit to configure the tuned model of the heat generation inventory, wherein the tuning includes a data matching process in which the measured values for selected parameters of the operating parameters are compared to predicted values for the selected parameters of the operating parameters to be a difference between them to determine on which the tuning of the model is based.

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法律状态:
2017-03-15| NV| New agent|Representative=s name: GENERAL ELECTRIC TECHNOLOGY GMBH GLOBAL PATENT, CH |

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2019-01-15| AZW| Rejection (application)|

优先权:

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申请号 | 申请日 | 专利标题

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US14/555,221|US20160147204A1|2014-11-26|2014-11-26|Methods and systems for enhancing control of power plant generating units|

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