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    method and system for controlling a wind farm "The present disclosure is directed to a system and method for controlling a wind farm. The method includes operating the wind farm based on multiple control definitions over a plurality of time intervals. The next step includes collecting one or more wind farm wind parameters in the plurality of time slots and one or more operating data points for each of the wind turbines in the wind farm for the plurality time slots. a contribution of the operating data points for each wind turbine as a function of one or more wind parameters. Additional steps of the method include estimating a wind farm's energy production for each of the control based definitions, at least partially on the contribution of the operating data points and controlling the wind farm based on definite ideal control options.
    公开号:BR102015009549A2
    申请号:R102015009549-0
    申请日:2015-04-28
    公开日:2018-02-27
    发明作者:Bhaskar Nitika；Krishnamurty Ambekar Akshay
    申请人:General Electric Company；
    IPC主号:
    专利说明:

    (54) Title: METHOD AND SYSTEM TO CONTROL A WIND FARM (51) Int. Cl .: F03D 7/02; F03D 7/04; F03D 9/25; H02J 3/38; H02P 101/15 (52) CPC: F03D 7/028, F03D 7/048, F03D 9/257, H02J 3/386, F05B 2270/20, H02P 2101/15 (30) Unionist Priority: 29/04/2014 IN 2156 / CHE / 2014 (73) Holder (s): GENERAL ELECTRIC COMPANY (72) Inventor (s): NITIKA BHASKAR; AKSHAY KRISHNAMURTY AMBEKAR (74) Attorney (s): CAROLINA NAKATA (57) Summary: METHOD AND SYSTEM FOR CONTROLING A WIND FARM This disclosure addresses a system and method for controlling a wind farm. The method includes operating the wind farm based on multiple control definitions at a plurality of time intervals. A next step includes collecting one or more wind parameters from the wind farm in the plurality of time intervals and one or more operating data points for each of the wind turbines in the wind farm for the plurality time intervals. The method also includes calculating a contribution from the operating data points for each of the wind turbines as a function of one or more wind parameters. Additional steps in the method include estimating energy production for the wind farm for each of the control definitions based, at least partially, on the contribution of the operating data points and controlling the wind farm based on ideal control definitions.
    Fig. 1
    1/21 "METHOD AND SYSTEM FOR CONTROLLING A WIND FARM" Field of the Invention [001] The present invention relates, in general, to wind turbines, and, more particularly, to systems and methods for controlling a wind farm.
    Background to the Invention [002] Wind energy is currently considered one of the purest, least damaging sources of energy available to the environment, and attention is increasingly focused on wind turbines. A modern wind turbine typically includes a tower, a generator, a gearbox, a nacelle and a rotor that has one or more rotor blades. Rotor blades transform wind energy into mechanical rotational torque that drives one or more generators through the rotor. The generators are sometimes, but not always, rotatably coupled to the rotor through the gearbox. The gearbox raises the inherently low rotational speed of the rotor so that the generator efficiently converts the rotational mechanical energy into electrical energy, which is fed to a utility grid through at least one electrical connection. Such configurations may also include power converters that are used to convert a frequency of electrical power generated to a frequency substantially similar to a frequency of utility power.
    [003] A plurality of wind turbines are used normally combined with each other to generate electricity and are commonly referred to as a "wind farm." Wind turbines in a wind farm typically include their own weather monitors that perform, for example, measurements of temperature, wind speed, wind direction, barometric pressure and / or air density. In addition, a separate weather mast or tower (“weather mast) is
    Petition 870170103093, of 12/28/2017, p. 9/40
    2/21 higher quality meteorological instruments that can provide more accurate measurements at a certain point in the park. The correlation of meteorological data with power output allows the empirical determination of a “power curve” for individual wind turbines.
    [004] Traditionally, wind farms are controlled in a decentralized manner to generate power so that each turbine is operated to maximize the local energy output and to minimize the impacts of local fatigue and extreme loads. However, in practice, such independent optimization of wind turbines ignores park-level performance targets, thereby leading to under-ideal performance in relation to the level of the wind farm. In addition, conventional wind turbine systems cannot cope with the prevailing wind influx and other environmental conditions. Since the environmental conditions on the wind farm tend to change frequently, the initial models estimated to operate the wind farm may be inaccurate for use during real-time deployment. Inaccurate modeling of wind parameters, in turn, can result in the use of incorrect control settings for wind turbines in the wind farm. As such, conventional optimization approaches for controlling a wind farm usually provide only marginal improvement in output from park level performance.
    [005] Thus, a system and method for controlling a wind farm that provides a framework to better manage the exchange between data availability and / or data quality of each of the wind turbines in the wind farm and energy estimates would be advantageous.
    Description of the Invention [006] The aspects and advantages of the invention will be partially presented in the description below, may be obvious from the description, or can be learned through the practice of the invention.
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    3/21 [007] In one aspect, the present invention is directed to a method for controlling the wind farm that has a plurality of wind turbines. The method includes a step to operate the wind farm based on multiple control definitions at a plurality of time intervals. A next step includes collecting one or more wind parameters from the wind farm in the plurality of time intervals. Another step includes collecting one or more operating data points for each of the wind turbines in the wind farm for the plurality of time intervals. The method also includes a step of calculating a contribution from the operating data points for each of the wind turbines as a function of one or more wind parameters. Additional steps in the method include estimating energy production for the wind farm for each of the control definitions based, at least partially, on the contribution of operating data points for each of the wind turbines and controlling the wind farm based on control settings that provide optimal energy production.
    [008] In another aspect, a system for controlling a wind farm that includes a plurality of wind turbines is revealed. The system includes a processor coupled communicatively to one or more sensors and a controller coupled communicatively to the processor. The processor is configured to: operate the wind farm based on multiple control settings in a plurality of time slots, collect one or more wind parameters from the wind farm in the plurality of time slots, collect one or more data points from operation for each of the wind turbines in the wind farm for the plurality of time intervals, calculate a contribution from the operating data points for each of the wind turbines as a function of one or more wind parameters and estimate an energy production for the wind farm for each of the control definitions based, at least partially, on the contribution of the
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    4/21 operating data for each of the wind turbines. In addition, the controller is configured to control the wind farm based on the definition of control with optimal energy production.
    [009] In yet another aspect, the present invention is directed to a method for controlling the wind farm that includes a plurality of wind turbines. The method includes (a) operating the wind farm based on the first control definitions in a first time interval; (b) collect one or more wind parameters for the wind farm during the first interval of time; (c) collect one or more operating data points for the first time interval for each of the wind turbines in the wind farm;
    (d) calculating a contribution from the operating data points for each of the wind turbines for the first time interval as a function of one or more wind parameters; (e) estimate energy production for the wind farm for the first time interval; (f) repeating steps (a) to (e) for the second control definitions for a second time interval; (g) comparing the energy production of the first control definitions with the energy production of the second control definitions to determine optimal control definitions; and, (h) control the wind farm based on the ideal control definitions.
    [010] These and other features, aspects and advantages of the present invention will be better understood with reference to the description below and the appended claims. The attached drawings, which are incorporated into the specification and form part of it, illustrate the realizations of the invention and, together with the description, serve to explain the principles of the invention.
    Brief Description of the Drawings [011] A complete and enabling description of the present invention, including its best mode, directed to a technician in the subject, is presented in this specification, which makes reference to the Figures
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    5/21 annexes, in which:
    Figure 1 shows a perspective view of an embodiment of a wind turbine;
    Figure 2 illustrates a schematic view of an embodiment of a controller for use with the wind turbine shown in Figure 1;
    Figure 3 illustrates a schematic view of an implementation of a wind farm, according to the present invention;
    Figure 4 shows a schematic view of an embodiment of a processor, according to the present invention;
    Figure 5 illustrates a graph of an energy production realization (geometric axis y) for multiple control definitions as a function of one or more wind parameters (geometric axis x) according to the present invention;
    Figure 6 shows a graph of an embodiment of a power curve model with the power along the geometric axis y and the wind speed along the geometric axis x, according to the present invention; and, Figure 7 illustrates a flow chart of an embodiment of a method, according to the present invention.
    Description of Embodiments of the Invention [012] Reference will now be made in detail to the embodiments of the invention, one or more of which are illustrated in the drawings. Each example is provided for purposes of explaining the invention, not to limit the invention. In fact, it will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the scope of the invention. For example, the features illustrated or described as part of one realization can be used with another realization to generate another additional realization. Accordingly, the present invention is intended to cover such modifications and variations, since they fall within the scope of the
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    6/21 attached claims and their equivalents.
    [013] In general, the present invention is directed to a system and method for controlling a wind farm that includes a plurality of wind turbines. For example, in one embodiment, the system operates the wind farm based on multiple control definitions at a plurality of time intervals and determines which of the control definitions are ideal As used in this document, the term “ideal control definitions” or variations thereof must encompass one or more control definitions that provide the greatest energy output for the wind farm, while also maintaining the loads suffered by each of the wind turbines in the wind farm below a predetermined threshold. More specifically, the system collects one or more wind parameters (for example, wind speed and / or wind direction) for the wind farm in the plurality of time intervals and one or more operating data points for the plurality time intervals for each of the wind turbines in the wind farm. In one embodiment, the system collects data using a supervisory control and data acquisition server (SCADA) at the wind farm. The system then processes the data by calculating a contribution from the operating data points for each of the wind turbines as a function of one or more wind parameters. In additional achievements, the system also estimates energy production (for example, Annual Energy Production (AEP)) for the wind farm for each of the control definitions based, at least partially, on the contribution of operation for each of the wind turbines. In certain embodiments, the system can also estimate a statistical confidence in the estimated energy production for the wind farm. Thus, the system is configured to control the wind farm based on the control settings that provide the ideal energy production, considering both the
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    7/21 data availability regarding data quality.
    [014] The various achievements of the system and method described in this document provide numerous advantages absent in the state of the art. For example, the present invention provides a systematic solution for controlling a wind farm that deals with data quality analysis and the uncertainty limits detailed at the park level. The uncertainty analysis provides a desired confidence in relation to the future performance of the wind farm. In addition, the present invention is configured to use the maximum amount of data collected, while ensuring that the data quality of the estimated energy production is not affected. In this way, the present system corrects data quality problems that originate at a park level, thereby addressing several challenges associated with modeling the park level. In addition, the inventors of the present invention have found that the power curves at the park level and the energy production estimates may not be well suited for different types of wind farms. Therefore, the present invention does not depend on specific park details and can dynamically select the most appropriate method or algorithm to calculate energy production based on the performance of desired metrics. Consequently, the present invention can be installed easily from one park to another.
    [015] Now, with reference to the drawings, Figure 1 illustrates a perspective view of an embodiment of a wind turbine 10 configured to implant the control technology, according to the present invention. As shown, the wind turbine 10 generally includes a tower 12 that extends from a support surface 14, a nacelle 16 mounted on the tower 12 and a rotor 18 coupled to nacelle 16. Rotor 18 includes a rotating hub 20 and at least one rotor blade 22 coupled to the hub and extending outside the hub 20. For example, in the illustrated embodiment, rotor 18 includes three blades
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    8/21 of rotor 22. However, in an alternative embodiment, rotor 18 may include more or less than three rotor blades 22. Each rotor blade 22 can be spaced in relation to hub 20 to facilitate rotation of rotor 18 to enable kinetic energy to be transformed from the wind into usable mechanical energy and subsequently into electrical energy. For example, hub 20 may be rotatably coupled to an electrical generator (not shown) positioned within nacelle 16 to allow electrical energy to be produced.
    [016] Wind turbine 10 may also include a wind turbine controller 26 centralized within nacelle 16. However, in other embodiments, controller 26 may be located within any other component of wind turbine 10 or at an outdoor location of the wind turbine. In addition, controller 26 can be communicatively coupled to any number of components of the wind turbine 10 in order to control the operation of such components and / or to implement a control action. For example, controller 26 may include a computer or other suitable processing unit. Thus, in various embodiments, controller 26 may include appropriate computer-readable instructions that, when deployed, configure controller 26 to perform a number of different functions, such as receiving, transmitting and / or executing wind turbine control signals. Consequently, controller 26 can generally be configured to control the wind turbine 10 operating modes (for example, start and shutdown sequence), decrease or increase the wind turbine 10 and / or control various components of the wind turbine 10 For example, controller 26 can be configured to control the blade pitch or pitch angle of each of the rotor blades 22 (i.e., an angle that determines a perspective of the rotor blades 22 in relation to the wind direction. ) to control the power output generated by the wind turbine 10 by adjusting
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    9/21 an angular position of at least one rotor blade 22 with respect to the wind. For example, controller 26 can control the pitch angle of the rotor blades 22 by rotating the rotor blades 22 around a step 28 geometry axis, both individually and simultaneously, by transmitting the control signals suitable for a drive angle or a pitch adjustment mechanism (not shown) of the wind turbine 10.
    [017] Now, with reference to Figure 2, a block diagram of an embodiment of suitable components that may be included within controller 26 is illustrated in accordance with aspects of the present invention. As shown, controller 26 may include or more processor (s) 58 and associated memory device (s) 60 configured to perform a variety of computer-implemented functions (for example, performing the methods, steps, calculations and the like disclosed in this document). As used in this document, the term “processor” refers not only to integrated circuits referred to in the prior art as being included in a computer, but also to a controller, a microcontroller, a microcomputer, a programmable logic controller (PLC), application specific integrated circuit, application specific processors, digital signal processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Port Arrays (FPGAs) and / or any other programmable circuits. In addition, memory device (s) 60 may generally include memory element (s) including, but not limited to, a computer-readable medium (for example, random access memory (RAM)) , a computer readable non-volatile medium (for example, a flash memory), one or more hard disk drives, a floppy disk, a compact disk with read-only memory (CD-ROM), rewritable compact disk drives (CDR / W), a magnetic optical disc (MOD), a versatile digital disc (DVD),
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    10/21 flash drives, optical drives, solid state storage devices and / or other suitable memory elements.
    [018] In addition, controller 26 may also include a communications module 62 to facilitate communications between controller 26 and the various components of wind turbine 10. For example, communications module 62 may include a sensor interface 64 (for example, example, one or more analog to digital converters) to allow signals transmitted by one or more sensors 65, 66, 67 to be converted into signals that can be understood and processed by controller 26. Additionally, it should be noted that sensors 65 , 66, 67 can be communicatively coupled to the communications module 62 using any suitable means. For example, as shown in Figure 2, sensors 65, 66, 67 are coupled to sensor interface 64 via a wired connection. However, in alternative embodiments, sensors 65, 66, 67 can be coupled to sensor interface 64 via a wireless connection, for example, using any suitable wireless communication protocol known in the art. For example, communications module 62 can include the Internet, a local area network (LAN), wireless local area networks (WLAN), wide area networks (WAN), for example, Global Inoperability networks for Access by Microwave (WiMax), satellite networks, cellular networks, sensor networks, Ad-hoc networks and / or short-range networks. For example, processor 58 can be configured to receive one or more signals from sensors 65, 66, 67.
    [019] The sensors 65, 66, 67 can be any suitable sensors configured to measure any wind turbine 10 operating data points and / or wind parameters of the wind farm. For example, sensors 65, 66, 67 can include paddle sensors to measure a pitch angle of one of the rotor blades 22 or to measure a load acting on a
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    11/21 of the rotor blades 22; generator sensors to monitor the generator (eg torque, rotational speed, acceleration and / or power output) and / or multiple wind sensors to measure various wind parameters (eg wind speed, wind direction, etc. .). In addition, sensors 65, 66, 67 can be located close to the ground of wind turbine 10, in nacelle 16, on a meteorological mast of wind turbine 10, or at any other location in the wind farm.
    [020] It must be understood that any other quantity or other type of sensors can be used in any location. For example, sensors can be accelerometers, pressure sensors, strain gauges, angle of attack sensors, vibration sensors, MIMU sensors, camera systems, fiber optic systems, anemometers, pinwheels, Detection sensors and Sonic Range (SODAR), infra lasers, Light Detection and Reach sensors (LIDAR), radiometers, pitot tubes, radiosondes, other optical sensors, and / or any suitable sensors. It should be noted that, as used in this document, the term "monitor" and variations thereof indicates that several wind turbine sensors 10 can be configured to provide a direct measurement of the parameters being monitored or an indirect measurement of such parameters. In this way, sensors 65, 66, 67 can, for example, be used to generate signals in relation to the parameter being monitored, which can then be used by controller 26 to determine the actual condition.
    [021] Now, with reference to Figure 3, a wind farm 200 that is controlled according to the system and method of the present invention is illustrated. As shown, wind farm 200 may include a plurality of wind turbines 202, including wind turbine 10 described above and a park controller 222. For example, as shown in the illustrated embodiment, wind farm 200 includes twelve wind turbines, including the turbine
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    Wind 12/21 10. However, in other embodiments, wind farm 200 may include any number of wind turbines, such as less than twelve wind turbines or more than twelve wind turbines. In one embodiment, the controller 26 of the wind turbine 10 can be communicatively coupled to the park controller 222 via a wired connection, for example by connecting the controller 26 via suitable communication links 226 (for example, suitable cable). Alternatively, controller 26 can be communicatively coupled to controller park 222 via a wireless connection, for example, using any suitable wireless communication protocol known in the art. In addition, controller park 222 can be configured generally similar to controllers 26 for each of individual wind turbines 202 within wind farm 200.
    [022] In several embodiments, one or more of the wind turbines 202 in the wind farm 200 may include a plurality of sensors to monitor various operating data points or control settings of the individual wind turbines 202 and / or one or more wind parameters of wind farm 200. For example, as shown, each of the wind turbines 202 includes a wind sensor 216, such as an anemometer, or any suitable device, configured to measure wind speeds or any other wind parameter. For example, in one embodiment, wind parameters include information regarding at least one of or a combination of the following: a gust of wind, a wind speed, a wind direction, a wind acceleration, a wind turbulence , a sharp wind, a wind rotation, a gust of wind, SCADA information or the like.
    [023] As is generally understood, wind speeds can vary significantly over a 200 wind farm. So,
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    13/21 the 216 wind sensor (s) can allow the local wind speed in each wind turbine 202 to be monitored. In addition, wind turbine 202 may also include one or more additional sensors 218. For example, sensors 218 can be configured to monitor the electrical properties of the generator output of each wind turbine 202, such as current sensors, voltage sensors , temperature sensors or power sensors that monitor the power output directly based on current and voltage measurements. Alternatively, sensors 218 can include any other sensors that can be used to monitor the power output of a wind turbine 202. It should be understood that wind turbines 202 in wind farm 200 can include any other suitable sensor known in the prior art. to measure and / or to monitor wind parameters and / or wind turbine operating data points.
    [024] Now, with reference to Figure 4, a schematic view of an embodiment of a processor 68 of controller park 222 is illustrated in accordance with aspects of the present invention. Wind farm processor (s) 68 is (are) configured to perform any of the steps of the present invention, as described in this document. Since the independent optimization of wind turbines 202 may, in practice, additionally decrease the overall energy production of wind farm 200, it is desirable to configure the operation of wind turbines 202 so that the energy output at the park level, the AEP, fatigue loads and / or noise in wind farm 200 remain within the corresponding designated thresholds. In particular, it is desirable to continuously adjust the control settings of each of the interacting wind turbines 202 based on data availability (for example, by parameter, by time) and the quality of real-time analysis, so performance targets at the park level are consistently achieved. More specifically, as shown, the
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    Processor 68 is configured to operate wind farm 200 based on multiple control settings over a plurality of time slots. Accordingly, processor 68 is configured to collect one or more wind parameters 69 for wind farm 200 in a plurality of time slots and one or more operating data points 70 for the plurality of time slots for each of the wind turbines 202 in wind farm 200. In one embodiment, wind parameters 69 and / or operating data points 70 can be generated using one or more of the sensors (for example, using sensors 65, 66 , 67, 216, 218 or any other suitable sensor). Alternatively, wind parameters 69 and / or operating data points 70 can be estimated using a computer model inside processor 68.
    [025] In addition, processor 68 is configured to process wind parameters 69 and operating data points 70 in various ways. For example, in one embodiment, processor 68 may include one or more data quality algorithms configured to process operating data. In additional embodiments, processor 68 can be configured to filter, mediate and / or adjust one or more operating data points 70. More specifically, data quality algorithms can be configured to filter one or more outliers , account for data points that were not provided and / or any other appropriate processing step. Thus, data quality algorithms provide a framework to better monitor the exchange between data availability (for example, by parameter, by time) and the quality of analysis, as will be discussed in more detail below.
    [026] For example, in various embodiments, the algorithms process wind parameters 69 and operating data points 70 to determine an operational state for each of the wind turbines 202
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    15/21 during each plurality of time slots. For example, as shown in data processing table 72 of Figure 4, the sample operating data points for five different wind turbines 202 in wind farm 200 are illustrated. As shown, operating data points 70 may also include information regarding the wind turbine identifier, the power generated (in kilowatts, kW) and the state of operation. In additional embodiments, the operating data points 70 may include information regarding at least one of or a combination of the following: a step angle, a generator speed, a power output, a torque output, a ratio of peak speed, yaw alignment or misalignment, a wind turbine operating state, one or more control settings, a temperature, pressure, or the like, as well as information regarding any wind turbines out of operation at the wind farm.
    [027] Based on operating data points 70, wind farm processor 68 is configured to infer the operating status of each wind turbine 202, which indicates whether wind turbine 202 is operating in a normal state or out of range. normal. More specifically, a "normal state" indicates that wind turbine 202 is not generating power, according to its control definitions and / or its power curve. An "abnormal state" indicates that wind turbine 202 is not generating power according to its control settings and / or its power curve. Then, processor 68 can infer whether wind turbine 202 is operating normally, if it is inactive for routine maintenance, for out of routine maintenance, or for any other reason (for example, power failure, etc.), or if the wind turbine 202 is operating at some intermediate point between the normal state and the idle state.
    [028] For example, as shown in Figure 4, turbines
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    16/21 wind farms 1, 2 and 5 are generating power as expected (ie 800 kW, 820 kW and 830 kW, respectively), while wind turbines 2 and 4 are not generating any power (as indicated by 0 kW) . As such, processor 68 determines the operating status of wind turbines 202 based on the power generated (or any other operating data point described herein). Consequently, as shown, processor 68 determined that wind turbines 1, 2 and 5 are operating in a normal or standard state, while wind turbines 2 and 4 are inactive.
    [029] In additional realizations, data quality algorithms are configured to calculate a contribution from each of the wind turbines 202 in relation to the operating data points as a function of one or more wind parameters 69, for example, a wind speed. As used herein, the term “contribution” or variations thereof must cover the amount of operating data points 70 that is collected from each individual wind turbine 202 in wind farm 200, compared to other wind turbines 202 in wind farm 200. In other words, if a first wind turbine is operating in a normal state and a second wind turbine is operating in an abnormal state, the contribution of data collected by the first wind turbine will be greater than the contribution of the second turbine wind power. In addition, processor 68 can calculate a percentage of each contribution from each wind turbine 202 to a total contribution for all wind turbines 202 in wind farm 200 and apply a correction factor to each percentage. For example, processor 68 uses all collected data, but corrects data collected from wind turbines 202 that are not operating properly. In this way, processor 68 uses the maximum amount of data collected, instead of eliminating or removing the data collected
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    17/21 of 202 wind turbines operating in a nonstandard manner.
    [030] Still referring to the realization of Figure 4, processor 68 is then configured to estimate energy production 74 for wind farm 200 for each of the control definitions, based, at least partially, on the contribution of operating data points 70 for each of wind turbines 202. For example, as shown, energy production 74 for wind farm 200 is based on the processed data generated by wind turbines 1 to 5 combined with one or more parameters of wind 69. In additional realizations, processor 68 can also determine a plurality of power outputs 74 for each control definition using multiple data quality algorithms and dynamically determine an ideal power output from the plurality of energy productions based on various conditions, including, but not limited to, wind farm 200 site conditions, wind turbine operating parameters or data points 202, wind turbine model specifications 202, or similar. More specifically, in various embodiments, site conditions may also include a known geometric layout of wind farm 200, including the number of nearby wind turbines 202, the actual locations of wind turbines 202, the relative locations of upstream wind turbines and the downstream 202, and / or the wind farm terrain information 200. Thus, processor 68 is configured to dynamically select the most appropriate algorithm to calculate energy production 74 based on the performance of desired metrics.
    [031] Now, in reference to Figure 5, the wind farm processor 68 can evaluate more than one control definition and compare control definitions to determine an ideal control definition. For example, as shown, a graph 75 generated by processor 68 during data analysis and filtering at
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    18/21 particular wind is illustrated. As shown, graph 75 illustrates energy production (geometric axis y) for multiple control definitions as a function of one or more wind parameters (geometric axis x), for example, wind direction in degrees, according to present invention. More specifically, graph 75 includes the data generated from the operation in a first control definition 80 overlaid with the data generated from an operation in a second control definition 82. As shown, the first control definition 80 corresponds to greater production of general energy; therefore, in the illustrated embodiment, the definition of ideal control corresponds to the first definition of control 80.
    [032] With reference to Figures 4 and 6, in certain embodiments, the wind farm processor 68 can also generate a real-time power curve model 76 for wind farm 200 based on the estimated energy production 74 for the farm wind power 200 and dynamically control wind power 200 based on power curve model 76. For example, as shown in Figure 6, processor 68 evaluates a power curve 84 for the first control definition and a power curve 86 for a second control definition and determines which control definition is ideal. In addition, processor 68 can determine at least an estimate of a statistical confidence in the estimated energy outputs 74 and / or power curves 84, 86. The statistical confidence in the energy output 74 can be determined in several ways. For example, as shown in Figure 6, a +/- standard deviation (for example, +/- 10%), as represented by the dotted lines 85 and 87, is determined for each of the power curves 84, 86. In the realizations In addition, the statistical confidence of energy production can be determined using one of or a combination of the following: distribution metrics for operational parameters, analysis of the behavior of
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    19/21 operation through the various control definitions, a collapse point, an influence function, a compensated medium, a sensitivity curve, or any other suitable method and / or calculation known in the art to determine a confidence limit . For example, in one embodiment, the statistical confidence of energy production can be estimated by determining a distribution of at least a portion of the operating data points. In an additional realization, the behavior of the operating data points through the various control definitions can be analyzed to determine the data spread over time. In addition, the behavior of the data points can be analyzed by determining a bias or a biased and / or standard means of the operating data points. By understanding the trend of operating data points, processor 68 can estimate or predict how the data points will behave in a subsequent period of time. In addition, the definition of optimal control can be determined based on the trade-off between data availability and / or data quality analysis.
    [033] In addition, and in reference to Figure 4, processor 68 can implement one or more control actions in one or more of wind turbines 202 within wind farm 200 in order to control wind farm 200 and optimize AEP wind farm 200. In certain embodiments, for example, processor 68 can determine updated or optimized control definitions based on the power curve model 76. More specifically, optimized control definitions can include at least one of the following : change the pitch angle of a rotor blade, modify a generator torque, modify the generator speed, modify the power output, yaw a wind turbine nacelle, brake one or more wind turbine components, add or activate a air flow modifying element on a rotor blade surface, or the like.
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    20/21 [034] Referring to Figure 7, an embodiment of a method 100 for controlling a wind farm 200 is illustrated. As shown, method 100 includes a step 102 for operating the wind farm based on multiple control definitions at a plurality of time intervals. Another step 104 includes collecting one or more wind parameters for wind farm 200 at a plurality of time intervals. An additional step 106 includes collecting one or more operating data points for each of the wind turbines 202 in the wind farm 200 over a plurality of time slots. Method 100 also includes calculating 108 a contribution from operating data points for each of the wind turbines as a function of one or more wind parameters. A next step 110 includes estimating energy production for wind farm 200 for each of the control definitions based, at least partially, on the contribution of operating data points for each of the wind turbines 202. Another step 112 includes controlling the 200 wind farm based on control settings that provide optimal energy production.
    [035] The achievements of a wind farm, a controller for a wind farm and a method for controlling a wind farm are described in detail above. The method, the wind farm and the controller are not limited to the specific realizations described in this document, however, preferably, the components of the wind turbines and / or the controller and / or the steps of the method can be used independently and separately from other components and / or the steps described in this document. For example, the controller and method can also be used in combination with other power systems and methods and are not limited to practice with the wind turbine controller only, as described in this document. Preferably, the design can be deployed and used in connection with many other power system or wind turbine applications.
    Petition 870170103093, of 12/28/2017, p. 28/40
    21/21 [036] Although the specific features of various embodiments of the invention may be shown in some drawings and not in others, this is only for convenience. In accordance with the principles of the invention, any feature of a design can be referenced and / or claimed in combination with any feature of any other design.
    [037] The present description uses examples to reveal the invention, including the best way, and also to enable any person skilled in the art to practice the invention, including producing and using any devices or systems and carrying out any built-in methods. The patentable scope of the invention is defined by the claims and may include other examples that occur to those skilled in the art. Such other examples are intended to be within the scope of the claims, if they include structural elements that do not differ from the literal language of the claims, or if they include equivalent structural elements with non-substantial differences from the literal languages of the claims.
    Petition 870170103093, of 12/28/2017, p. 29/40
    1/3
    权利要求:
    Claims (2)
    [1]
    1/7
    Petition 870170103093, of 12/28/2017, p. 33/40
    1. METHOD (100) TO CONTROL A WIND FARM (200) which includes a plurality of wind turbines (10, 202), characterized by the fact that the method comprises:
    operating (102) the wind farm (200) based on multiple control definitions in a plurality of time intervals;
    collecting (104) one or more wind parameters (69) from the wind farm (200) in the plurality of time intervals;
    collecting (106) one or more operating data points (70) for each of the wind turbines (10, 202) in the wind farm (200) for the plurality of time intervals;
    processing the operating data points (70) for each of the wind turbines (10, 202) to determine an operational state of each of the wind turbines (10, 202) during each of the plurality of time slots;
    calculate (108) a contribution from the operating data points (70) for each of the wind turbines (10, 202) as a function of one or more wind parameters (69) comprising:
    determine a percentage of each contribution from each wind turbine (10, 202) to a total contribution to all wind turbines (10, 202) in the wind farm (200); and apply a correction factor to each percentage; estimate (110) an energy production for the wind farm (200) for each of the control definitions based, at least partially, on the contribution of the operating data points (70) for each of the wind turbines (10, 202 ); and, control (112) the wind farm (200) based on the control definitions that provide an ideal energy production.
    Petition 870170103093, of 12/28/2017, p. 30/40
    2/3
    2. METHOD (100), according to claim 1, characterized by the fact that it additionally comprises at least one among filtering the one or more operating data points (70), averaging the one or more points operating data (70), or adjust the one or more operating data points (70) to account for the data points that have been deprived.
    3. METHOD (100), according to claim 1 or 2, characterized by the fact that controlling the wind farm (200) additionally comprises estimating a power curve model in real time for the wind farm (200) with based on the ideal energy production and control the wind farm (200) based on the power curve model.
    4. METHOD (100), according to any one of claims 1 to 3, characterized by the fact that it additionally comprises determining at least an estimate of a statistical confidence in energy production for the wind farm (200) for each one control settings.
    5. METHOD (100), according to any one of claims 1 to 4, characterized by the fact that estimating energy production for wind farm (200) for each of the control definitions is still based, at least partially, on in one or more of the following: wind farm location conditions (200), wind turbine operating parameters (10, 202), wind turbine location in the wind farm or wind turbine model specifications.
    6. METHOD (100) according to any one of claims 1 to 5, characterized by the fact that the wind parameters comprise information in relation to at least one of or a combination of the following: a gust of wind, a speed of wind, a wind direction, a wind acceleration, a breath of wind, a sharp
    Petition 870170103093, of 12/28/2017, p. 31/40
    3/3 wind, wind rotation, wind trail or SCADA information.
    7. METHOD (100) according to any one of claims 1 to 6, characterized by the fact that the operating data points (70) comprise information in relation to at least one of or a combination of the following: an angle of step, a generator speed, a power output, a torque output, a peak speed ratio, a wind turbine operating state, one or more control settings, a temperature and a pressure.
    8. METHOD (100) according to any one of claims 1 to 9, characterized by the fact that controlling the wind farm (200) based on the control definitions that provide the ideal energy production comprises, additionally, implementing one or more more control actions in one or more of the wind turbines (10, 202) within the wind farm (200) in order to optimize the energy production of the wind farm (200), in which the one or more control actions comprise at least at least one among changing the pitch angle of a rotor blade (22), changing a generator torque, changing the generator speed, changing the power output, yawing a nacelle (16) of the wind turbine (10), braking a or more wind turbine components (10) or add or activate an airflow modifying element on a rotor blade surface (22).
    9. SYSTEM TO CONTROL A WIND FARM (200) that includes a plurality of wind turbines (10), characterized by the fact that the method comprises:
    a processor (68) communicatively coupled to one or more sensors (65, 66, 67, 216, 218), the processor being configured to implement a method (100) as defined in claims 1 to 8.
    Petition 870170103093, of 12/28/2017, p. 32/40
    [2]
    2/7
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    法律状态:
    2018-02-27| B03A| Publication of a patent application or of a certificate of addition of invention [chapter 3.1 patent gazette]|
    2018-10-30| B06F| Objections, documents and/or translations needed after an examination request according [chapter 6.6 patent gazette]|
    2020-05-12| B06U| Preliminary requirement: requests with searches performed by other patent offices: procedure suspended [chapter 6.21 patent gazette]|
    优先权:
    申请号 | 申请日 | 专利标题
    IN2156/CHE/2014|2014-04-29|
    IN2156CH2014|2014-04-29|
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