• 专利附录
  • 专利说明
  • 权利要求
  • 类似技术
  • 同族专利
  • 引用文献
  • 法律状态
  • 优先权
    • 专利摘要:
    The invention relates to a system (60) and a method for detecting an anomaly in a combustion section of a gas turbine. The system (60) includes a plurality of combustion chambers associated with a combustion section, a plurality of thermal sensors (50) disposed about an exhaust section (22) of a gas turbine, and a processing circuit (62) having a memory (70). The method includes receiving gas turbine engine exhaust profile data from the plurality of thermal sensors (50) disposed about the exhaust gas turbine exhaust section (22). The method also analyzes the exhaust profile data to compute statistical features associated with a peak-valley pattern. The method further determines, using a machine learning algorithm, that the statistical features are abnormal. In response to the determination, the method processes the exhaust profile data for a predetermined time period and reports an anomaly in a combustion section of the gas turbine if the statistical characteristics remain abnormal for the predetermined period of time.
    公开号:CH710737A2
    申请号:CH00136/16
    申请日:2016-02-02
    公开日:2016-08-15
    发明作者:Warren Miller Karen；Yu Lijie；Joseph Iasillo Robert
    申请人:Gen Electric；
    IPC主号:
    专利说明:

    TECHNICAL AREA
    [0001] The disclosure relates to the field of turbomachinery, and more particularly to systems and methods of gas turbine combustion profile monitoring.
    BACKGROUND
    Turbomachinery may include a compressor section linked to a turbine section by a common compressor / turbine shaft and a combustor assembly. An intake airflow may pass through an air inlet to the compressor section. In the compressor section, the intake airflow may be compressed to the combustor assembly through a number of consecutive stages. In the combustor assembly, the compressed air stream may mix with fuel to form a combustible mixture. The combustible mixture may be combusted in the combustor assembly to form hot gases. The hot gases can be passed along a hot gas path of the turbine section through a transition piece. The hot gases may expand along a hot gas path through a number of turbine stages, acting on wheel-mounted turbine blade vanes to produce work that is discharged to drive a generator, for example. The hot gases may pass from the turbine section through an exhaust system as exhaust gases. A number of thermocouples may be disposed in the exhaust system to measure temperatures of the exhaust gases.
    The measured by the thermocouples temperatures of the exhaust gases can form an exhaust profile. The exhaust profile can be used to assess the operability of incinerators. Certain system problems can cause a combustion chamber to run abnormally hot or cold, which can interfere with the typical exhaust profile. An atypical exhaust profile pattern may indicate abnormal operation of one or more combustion chambers. The typical exhaust profile for some turbomachinery is uniform, with individual exhaust thermocouples deviating only slightly from the mean. For such turbomachinery, detection of incinerator anomalies can be accomplished by identifying thermocouple groups that differ significantly from the mean. Other turbomachinery may have an exhaust profile that has a peak-valley pattern during normal operation. Typically, a number of peaks and a number of valleys in the peak-valley pattern correspond to the number of combustion chambers of the turbomachine. The approach described above may not be effective in detecting combustion anomalies for turbo machines having a peak-valley pattern because the peak-valley profile pattern may be treated as abnormal deviations from the mean.
    BRIEF SUMMARY OF THE INVENTION
    [0004] This invention relates to systems and methods for gas turbine combustion profile monitoring. Certain embodiments may facilitate the detection of anomaly in a combustion section of a gas turbine. According to an embodiment of the disclosure, a method of detecting an anomaly in a combustion section of a gas turbine includes receiving, by at least one processor, a plurality of thermal sensors disposed about an exhaust gas turbine exhaust section, the exhaust gas profile data of the gas turbine. The method may further include analyzing the exhaust profile data to compute statistical features associated with a peak-valley pattern. The method, using a machine learning algorithm, may facilitate the determination that the statistical features are abnormal. The method may further include, in response to the determination, processing the exhaust profile data for a predetermined period of time and reporting an anomaly in a combustion section of the gas turbine if the statistical features remain abnormal for the predetermined period of time.
    According to another embodiment of the disclosure, a system for detecting an anomaly in a combustion section of a gas turbine is provided. The system may include a plurality of combustors associated with a combustion section and a plurality of thermal sensors disposed about an exhaust section of a gas turbine. The thermal sensors are configured to provide exhaust profile data of the gas turbine. The system further includes a processing circuit communicatively coupled to a memory, the memory storing instructions that perform operations when executed by the processing circuitry.
    Other embodiments, systems, methods, features, and aspects will become apparent from the following description considered in conjunction with the following drawings.
    BRIEF DESCRIPTION OF THE DRAWINGS
    [0007]<Tb> FIG. 1 <SEP> is a block diagram of an exemplary gas turbine according to an embodiment of the disclosure.<Tb> FIG. 2 <SEP> is an axial view of an exemplary exhaust system of a gas turbine and a combustion anomaly detection system, according to an embodiment of the disclosure.<Tb> FIG. 3A <SEP> is a normal profile of thermocouple exhaust gas data, according to an embodiment of the disclosure.<Tb> FIG. 3B <SEP> is an abnormal profile of thermocouple exhaust gas data, according to an embodiment of the disclosure.<Tb> FIG. 4 <SEP> is a flowchart illustrating an exemplary method of detecting an anomaly in a combustion section of a gas turbine, according to an embodiment of the disclosure.<Tb> FIG. 5 <SEP> is a flowchart illustrating an exemplary method of identifying peak and valley locations in thermocouple exhaust gas data, according to one embodiment of the disclosure.<Tb> FIG. 6 <SEP> is a block diagram illustrating an exemplary controller for controlling a gas turbine, according to an embodiment of the disclosure.
    DETAILED DESCRIPTION
    The following detailed description includes references to the accompanying drawings, which form a part of the detailed description. The drawings depict illustrations, according to exemplary embodiments. These embodiments, also referred to herein as "examples," are described in sufficient detail to enable those skilled in the art to practice the present subject matter. The embodiments may be combined, other embodiments may be used, or structural, logical, and electrical changes may be made without departing from the scope of the claimed subject matter. The following detailed description is therefore not to be understood in a limiting sense, and the scope is defined by the appended claims and their equivalents.
    Certain embodiments of the disclosure relate to methods and systems for gas turbine combustion profile monitoring that may facilitate the detection of anomaly in a combustion section of a gas turbine engine. The disclosed methods and systems may also provide for determining a non-uniform temperature profile during operation of a gas turbine.
    [0010] In some embodiments of the disclosure, a processing circuit of a plurality of thermal sensors disposed about an exhaust gas turbine exhaust section may receive the exhaust gas profile data of the gas turbine. The exhaust profile data may be analyzed to calculate statistical features associated with a peak-valley pattern. The analysis may include, based on the exhaust profile data, identifying a peak-valley pair associated with each thermal sensor and calculating the statistical characteristics for each peak-valley pair. The processing circuitry may further determine, using a machine learning algorithm, that the statistical features are abnormal. In response to the determination, the processing of the exhaust profile data may continue for a predetermined period of time. If the statistical features remain abnormal for the predetermined period, an abnormality may be reported in a combustion section of the gas turbine.
    Technical effects of certain embodiments of the disclosure may include combustion monitoring of gas turbines. Other technical implications of certain embodiments of the disclosure may increase the likelihood of detecting combustion anomalies in gas turbines before the combustion anomalies can lead to significant events or equipment failures. The disclosed embodiments of the disclosure may provide insight into the combustion stability of the gas turbines to reduce combustion related errors, forced downtime, and unplanned costs.
    The following provides the detailed description of various embodiments relating to systems and methods for modeling operational impact using statistical and physics-based methodologies.
    A gas turbine engine plant according to one embodiment of the disclosure is illustrated in FIG. 1, generally at 2. The gas turbine engine plant 2 may include a gas turbine engine 4 having a compressor section 6 in fluid communication with a turbine section 8 through a combustor assembly 10. The combustor assembly 10 may include one or more combustors 12 that may be arranged in a tubular-annular array. The compressor section 6 may also be mechanically linked to the turbine section 8 by a shaft 14. The compressor section 6 may include an air inlet 16, and the turbine section 8 may include an exhaust gas outlet 18. An air intake system 20 may be in fluid communication with the air inlet 16. The air intake system 20 can treat air that passes into the compressor section 6. For example, the air intake system 20 may remove or reduce moisture carried by air passing into the air inlet 16. An exhaust system 22 may be in fluid communication with the exhaust outlet 18. The exhaust system 22 may treat exhaust gases passing from the turbine section 8 prior to introduction into an environment. The exhaust system 22 may include a plurality of temperature sensors 50. The gas turbine engine system 2 may also include a driven load 30, which could take the form of a generator, a pump or a vehicle. The gas turbine engine system 2 may further include a combustor anomaly detection system 60 operatively connected to an alarm 74.
    As shown in FIG. 2, the exhaust system 22 may include a housing 40 having an outer surface 42 and an inner surface 44 defining an exhaust flow path 46. The exhaust system 22 may include a plurality of temperature sensors, one of which is indicated at 50, disposed on the housing 40. The temperature sensors 50 may take the form of thermocouples circumferentially grouped about the inner surface 44 and exposed to the exhaust flow path 46. In one embodiment of the disclosure, the combustor abnormality detection system 60 is operatively connected to each of the plurality of temperature sensors 50. It should be understood that combustor anomaly detection system 60 may be co-located with gas turbine engine 4, integrated with the turbine controller, or located in a central general monitoring station. Thus, the combustor anomaly detection system 60 may receive data from multiple gas turbine engine systems located anywhere in the world and monitor it simultaneously from a single monitoring location. The combustor anomaly detection system 60 may include a central processing unit (CPU) 62 and a computer-readable storage medium 64 provided with a set of program instructions 68 and a memory 70. As will be discussed in greater detail below, the combustor abnormality detection system 60 may be operatively connected to an alarm 74 that may provide a visual and / or audible alarm upon detection of a combustion anomaly.
    FIGS. 3A and 3B illustrate two exemplary profiles of thermocouple exhaust gas data, according to one embodiment of the disclosure. The data can be represented as diagrams in polar coordinates. An angular coordinate of a point on the graph may correspond to the angle of a temperature sensor of the plurality of temperature sensors. A radius of the point may correspond to temperature data provided by the temperature sensor.
    FIG. 3A illustrates an exemplary normal profile of thermocouple exhaust gas data for a turbomachine including six combustors. The normal profile may correspond to normal operation of the combustors. In the example of FIG. 3A, the normal profile 6 includes tips corresponding to six combustors of the gas turbine engine 2. The six peaks alternate with six valleys. In total, six peaks and six valleys can form a normal peak-and-valley pattern for six-chamber turbomachinery.
    FIG. 3B illustrates an exemplary abnormal profile of thermocouple exhaust gas data for a turbomachine including six combustors. Unlike the normal profile of FIG. 3A, the abnormal profile of FIG. 3B lacks a tip at region 80. FIG Absence of a peak in a tip-valley pattern may indicate anomaly in operations of combustors of the turbomachine 2.
    FIG. 4 is a flowchart illustrating an exemplary method 400 for detecting an anomaly in a combustion section of a gas turbine, according to an embodiment of the disclosure. The operations of the method 400 may be performed by the combustor abnormality detection system 60. The operations of the method 400 may be embedded in program instructions 68 of the combustor anomaly detection system 60. Method 400 may analyze thermocouple exhaust gas data to detect an abnormal tip-valley pattern. In some embodiments of the disclosure, the system 60 is configured to perform processing of the exhaust gas data once per minute.
    In block 402, the combustor anomaly detection system 60 may receive thermocouple TC data from the thermometer sensors 50. At block 404, the system 60 may perform data quality checks. At decision block 406, the system 60 may determine whether the turbomachine is running above a predetermined load at which a peak-to-valley pattern is to be expected. If the turbomachinery is not running above the predetermined load, then at block 408, the system 60 may proceed to evaluate subsequent data (eg, in a subsequent minute of received thermocouple exhaust gas data).
    If the turbomachine is running above the predetermined load, then at block 410 system 60 may calculate the deviation from the average exhaust gas temperature for each thermocouple. At block 412, the system 60 may identify peaks and valleys in exhaust gas data. In block 414, the system 60 may calculate statistical features (eg, a feature vector) for the peak-valley pairs. The statistical characteristics can be the peak-to-valley temperature difference (delta), the mean deviation from all peaks or valleys, the minimum peak temperature, the maximum peak temperature, the minimum valley temperature, the maximum valley temperature, the peak-to-valley maximum delta, the minimum delta from tip to valley and so on.
    At decision block 416, the system 60 may determine if the profile corresponding to the thermocouple exhaust gas data is abnormal. In some embodiments of the disclosure, the determination includes processing the feature vector evaluated at block 414 by a machine-learned classification model. It should be appreciated by those skilled in the art that the type of suitable classification model may include, but is not limited to, a support vector machine (SVM), an artificial neural network (ANN), a decision tree, or other classifiers , The model can be trained offline using both normal exhaust gas data samples and outage exhaust gas data samples. By processing the feature vector using the trained model, the system 60 may determine if there is an abnormal peak-to-valley pattern in the thermocouple exhaust data. If the profile (peak-valley pattern) is normal, then system 60 may proceed to block 418 with the evaluation of subsequent thermocouple exhaust data.
    If the profile is abnormal, the system 60 may count the persistence of the abnormality and evaluate the latching of the abnormality. If the abnormal tip-valley pattern exists for a predefined period of time and an alarm has not already been generated during a predefined latching period, an alarm may be raised for further evaluation and action in block 422.
    FIG. 5 is a flowchart illustrating an exemplary method 500 for identifying peak and valley locations in thermocouple exhaust gas data, according to an embodiment of the disclosure. The method 500 may provide details of the block 412 of the method 400 shown in FIG. 4. At block 502, the method 500 may include receiving thermocouple exhaust gas data. The thermocouple data may include temperatures Y (i) provided by the temperature sensors 50 for a given minute. At block 504, the method 500 may include detecting positions of peaks. The position of peaks can be defined by the condition Y (i)> Y (i-1) and Y (i)> Y (i + 1). At block 506, the method 500 may include detecting positions of valleys. Each of the valleys can be defined as the minimum Y (i) between two consecutive peaks. At block 508, the method 500 may include entering unrecognized peaks based on gaps in the spatial position. At block 510, method 500 may include performing data correction if more than M peaks are detected, where M is the number of combustors in the turbomachine. At block 512, the method 500 may provide for outputting the final peak and valley positions.
    FIG. 6 is a block diagram illustrating an exemplary controller 600 for detecting an anomaly in a combustion section, according to an embodiment of the disclosure. Specifically, the elements of the controller 600 may be used to operate a gas turbine under a plurality of operating conditions, but within predetermined operational limits of combustion, to automatically collect operational data associated with the gas turbine while the gas turbine is running to store the operational data. to generate a set of constants for one or more predetermined combustion transfer functions based on the operational data and to store the set of constants in the gas turbine combustion control system to be used during start-up of the gas turbine. The controller 600 may include a memory 610 that stores programmed logic 620 (e.g., software) and may store data 630 such as operational data associated with the gas turbine, the set of constants, and the like. The memory 610 may also include an operating system 640.
    A processor 650 may use the operating system 640 to execute the programmed logic 620, and may also use the data 630 as well. A data bus 660 may provide communication between the memory 610 and the processor 650. Users may connect to the controller 600 via at least one user interface device 670, such as a keyboard, mouse, control panel, or any other device capable of forwarding data to and from the controller 600. The controller 600 may be in line with the gas turbine combustion control system via an input / output (I / O) interface 680 while it is operating and offline while in communication with the gas turbine combustion control system, while not is working. In addition, it should be appreciated that other external devices or multiple other gas turbines or combustors may communicate with the controller 600 via the I / O interface 680. In the illustrated embodiment of the disclosure, the controller 600 may be remotely located with respect to the gas turbine, but may be co-located with or even integrated with the gas turbine. Further, the controller 600 and the programmed logic 620 implemented thereby may include software, hardware, firmware, or any combination thereof. It should also be appreciated that multiple controllers 600 may be used whereby different features described herein may be performed on one or more different controllers 600.
    Reference will be made to block diagrams of systems, methods, apparatus, and computer programs, in accordance with embodiments of the disclosure. It is understood that at least some of the blocks of the block diagrams and combinations of blocks in the block diagrams may be at least partially implemented by computer program instructions. These computer program instructions may be loaded on a general purpose computer, a special purpose computer, a special software based computer or other programmable data processing device to produce a machine such that the instructions executed on the computer or other programmable data processing device comprise means for To implement the functionality of at least some of the blocks of the block diagrams or to create combinations of blocks in the block diagrams.
    These computer program instructions may also be stored in a computer readable memory which may instruct a computer or other programmable data processing device to function in a particular manner such that the instructions stored in the computer readable memory produce an article of manufacture which includes instruction means. which implement the function specified in the block or blocks. The computer program instructions may also be loaded on a computer or other programmable data processing device to cause a series of operations to be performed on the one computer or other programmable data processing device to generate a computer-implemented process such that the instructions, the be executed on the computer or other programmable data processing device, provide steps to implement the functions specified in the block or blocks.
    One or more components of the systems and one or more elements of the methods described herein may be implemented by an application program running on an operating system of a computer. They may also be put into practice with other computer system configurations, including hand-held devices, multiprocessor systems, microprocessor-based or programmable consumer electronics, mini-computers, mainframe computers, and the like.
    Application programs, which are components of the systems and methods described herein, may include routines, programs, components, data structures, and so on, that implement particular abstract data types and perform certain tasks or actions. In a distributed computing environment, the application program may be (in whole or in part) in local storage or other storage. Additionally or alternatively, the application program may be located (in whole or in part) in remote storage or storage to allow for circumstances where tasks are performed by remote processing devices linked through a communications network.
    Many modifications and other embodiments of the exemplary descriptions set forth herein to which these descriptions pertain will become apparent, given the benefit of the teachings presented in the foregoing descriptions and the accompanying drawings. It will thus be appreciated that the disclosure can be embodied in many forms and not limited to the embodiments described above.
    Therefore, it should be understood that the disclosure is not to be limited to the specific embodiments disclosed and that it is intended that modifications and other embodiments be included within the scope of the appended claims. Although specific terms are used herein, they are used in a generic and descriptive sense only and not for purposes of limitation.
    LIST OF REFERENCE NUMBERS
    [0032]<Tb> 2 <September> gas turbo machinery<Tb> 4 <September> Gas turbomachinery<Tb> 6 <September> compressor section<Tb> 8 <September> turbine section<Tb> 10 <September> combustion chamber assembly<Tb> 12 <September> combustion chambers<Tb> 14 <September> wave<Tb> 16 <September> air intake<Tb> 18 <September> exhaust outlet<Tb> 20 <September> Air intake system<Tb> 22 <September> Exhaust system<tb> 30 <SEP> driven load<Tb> 40 <September> Housing<Tb> 42 <September> outer face<Tb> 44 <September> inner surface<Tb> 46 <September> exhaust flow path<tb> 50 <SEP> several temperature sensors<Tb> 60 <September> combustion anomaly detection system<tb> 62 <SEP> central processing unit (CPU)<tb> 64 <SEP> computer-readable storage medium<tb> 68 <SEP> set of program statements<Tb> 70 <September> Memory<Tb> 74 <September> Alarm<Tb> 80 <September> Area<Tb> 400 <SEP> A method of detecting an abnormality in a combustion section of a gas turbine<Tb> 402 <September> Process Operation<Tb> 404 <September> Process Operation<Tb> 406 <September> Process Operation<Tb> 408 <September> Process Operation<Tb> 410 <September> Process Operation<Tb> 412 <September> Process Operation<Tb> 414 <September> Process Operation<Tb> 416 <September> Process Operation<Tb> 418 <September> Process Operation<Tb> 420 <September> Process Operation<Tb> 422 <September> Process Operation<tb> 500 <SEP> Method of identifying peak and valley locations in thermocouple exhaust gas data<Tb> 502 <September> Process Operation<Tb> 504 <September> Process Operation<Tb> 506 <September> Process Operation<Tb> 508 <September> Process Operation<Tb> 510 <September> Process Operation<Tb> 512 <September> Process Operation<Tb> 600 <September> Control Unit<Tb> 610 <September> Memory<tb> 620 <SEP> programmed logic<Tb> 630 <September> Data<Tb> 640 <September> Operating system<Tb> 650 <September> Processor<Tb> 660 <September> data bus<Tb> 670 <September> user interface device<tb> 680 <SEP> input / output (I / O) interface
    权利要求:
    Claims (14)
    [1]
    A system (60) for detecting an anomaly in a combustion section (10) of a gas turbine (4), the system comprising:a plurality of combustion chambers (12) associated with a combustion section (10),a plurality of thermal sensors (50) disposed about an exhaust section (22) of a gas turbine (4), the thermal sensors (50) being configured to provide exhaust gas profile data of the gas turbine (4),a processing circuit (62) communicatively coupled to a memory (70), the memory (70) storing instructions (68) which, when executed by the processing circuit (62), perform operations comprising:receiving, from the plurality of thermal sensors (50), the exhaust gas profile data of the gas turbine (4),analyzing the exhaust profile data to compute statistical features associated with a peak-valley pattern,determining, using a machine learning algorithm, that the statistical features are abnormal, andin response to the determination, continuing to process the exhaust profile data for a predetermined time period and reporting an anomaly in the combustion section (10) of the gas turbine (4) if the statistical features remain abnormal for the predetermined period of time.
    [2]
    The system (60) of claim 1, wherein the machine learning algorithm is trained using historical exhaust profile data, the historical exhaust profile data including normal data samples and incorrect data samples.
    [3]
    The system (60) of claim 1, wherein the plurality of thermal sensors (50) are disposed radially about an exhaust gas diffuser associated with the gas turbine (4).
    [4]
    The system (60) of claim 1, wherein the plurality of thermal sensors (50) are arranged in a uniformly spaced array.
    [5]
    The system (60) of claim 1, wherein the exhaust profile data includes a plurality of peak and valley pairs, each peak and valley pair of the plurality of peaks and valleys corresponding to at least one combustion chamber of the plurality of combustors (12).
    [6]
    The system (60) of claim 5, wherein said analyzing includes evaluating each pair of peak and valley in relation to an expected peak-valley pattern.
    [7]
    The system (60) of claim 5, wherein the analyzing includes:identifying, based on the exhaust profile data, a pair of peak and valley associated with each of the plurality of thermal sensors (50)calculating the statistical features for each peak-valley pair.
    [8]
    The system (60) of claim 1, wherein the exhaust profile data includes statistical characteristics associated with the plurality of thermal sensors (50), the statistical features including at least one of the following: a minimum peak temperature, a maximum peak temperature, a minimum target temperature, a maximum valley temperature, a minimum peak-to-valley delta and a maximum delta-to-valley delta.
    [9]
    The system (60) of claim 1, wherein determining that the statistical features are abnormal includes generating a feature vector based on at least the statistical features and processing the feature vector by a classification model.
    [10]
    The system (60) of claim 1, further comprising, prior to analyzing:determining a quality of the exhaust profile data andin response to determining that the quality is below a predetermined quality level, adjusting the exhaust profile data.
    [11]
    The system (60) of claim 1, wherein the analyzing is performed after the gas turbine (4) operates above a predetermined load.
    [12]
    The system (60) of claim 1, further comprising outputting an alarm (74) based at least in part on detecting the anomaly in the combustion section (10) of the gas turbine (4).
    [13]
    The system (60) of claim 12, wherein the alarm (74) triggers at least one of the following: a further evaluation and a responding action.
    [14]
    14. A method (400) for detecting an anomaly in a combustion section (10) of a gas turbine (4), the method comprising:receiving, from a plurality of thermal sensors (50), which are arranged around an exhaust gas section (22) of a gas turbine (4), the exhaust gas profile data of the gas turbine (4),analyzing the exhaust profile data to compute statistical features associated with a peak-valley pattern,determining, using a machine learning algorithm, that the statistical features are abnormal, andin response to the determination, continuing to process the exhaust profile data for a predetermined period of time andreporting an anomaly in a combustion section (10) of the gas turbine (4) if the statistical features remain abnormal for the predetermined period of time.
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    法律状态:
    2017-03-15| NV| New agent|Representative=s name: GENERAL ELECTRIC TECHNOLOGY GMBH GLOBAL PATENT, CH |
    2019-05-15| AZW| Rejection (application)|
    优先权:
    申请号 | 申请日 | 专利标题
    US14/616,105|US9791351B2|2015-02-06|2015-02-06|Gas turbine combustion profile monitoring|
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