Generator stator partial discharge detection threshold optimization method and device, equipment, storage medium and program product

Abstract

The invention relates to a generator stator partial discharge detection threshold optimization method and device, equipment, a storage medium and a program product, and relates to the technical field of power equipment state monitoring. The method can improve the dynamic adaptability of threshold adjustment. The method comprises the following steps: carrying out signal preprocessing on an original partial discharge signal collected by a partial discharge sensor of a generator to be detected to obtain a target partial discharge signal; performing feature extraction and pattern recognition on the target partial discharge signal to obtain a statistical feature and a frequency domain feature of the target partial discharge signal and a discharge type of generator stator partial discharge; according to the statistical characteristics, the frequency domain characteristics and the discharge type, the false alarm rate and the missing report rate of partial discharge of the generator stator are calculated through a stochastic gradient descent algorithm, and a comprehensive loss function is determined according to the false alarm rate and the missing report rate; and optimizing the initial detection threshold according to the learning rate of the stochastic gradient descent algorithm and the comprehensive loss function to obtain an updated target detection threshold.

Description

Technical Field

[0001] The present application relates to the technical field of power equipment status monitoring, and in particular to a method, device, computer equipment, computer-readable storage medium, and computer program product for optimizing a generator stator partial discharge detection threshold. Background Art

[0002] Generator stator partial discharge (PD) refers to the occurrence of partial discharge within a motor's stator windings or between the windings and the stator core. This phenomenon is typically caused by the deterioration or aging of electrical insulation materials and is a common type of generator insulation failure. Generator stator partial discharge is a key sign of insulation degradation, and its detection is crucial for preventing equipment failure.

[0003] Traditional detection methods mainly use static model methods to set and adjust partial discharge detection thresholds. However, this method relies on offline training data and is difficult to cope with real-time operating condition fluctuations. In addition, model updates require manual intervention and have poor dynamic adaptability. Summary of the Invention

[0004] Based on this, it is necessary to provide a method, device, computer equipment, computer-readable storage medium and computer program product for optimizing the stator partial discharge detection threshold of a generator in order to address the above technical problems.

[0005] In a first aspect, the present application provides a method for optimizing a generator stator partial discharge detection threshold, comprising:

[0006] Acquiring an original partial discharge signal collected by a partial discharge sensor of the generator to be tested, performing signal preprocessing on the original partial discharge signal to obtain a target partial discharge signal;

[0007] Performing feature extraction and pattern recognition on the target partial discharge signal to obtain statistical features, frequency domain features, and discharge type of the generator stator partial discharge;

[0008] Calculating the false alarm rate and missed alarm rate of the generator stator partial discharge using a stochastic gradient descent algorithm based on the statistical characteristics, the frequency domain characteristics, and the discharge type, and determining a comprehensive loss function based on the false alarm rate and the missed alarm rate;

[0009] An initial detection threshold is obtained, and the initial detection threshold is optimized according to the learning rate of the stochastic gradient descent algorithm and the comprehensive loss function to obtain an updated target detection threshold.

[0010] In one embodiment, the optimizing the initial detection threshold according to the learning rate of the stochastic gradient descent algorithm and the comprehensive loss function to obtain an updated target detection threshold includes:

[0011] The initial detection threshold, the learning rate of the stochastic gradient descent algorithm, and the comprehensive loss function are input into a preset threshold optimization model for threshold optimization calculation to obtain a threshold target amount output by the threshold optimization model; a threshold update configuration file is generated based on the threshold target amount; in response to a threshold update instruction sent by the server, the initial detection threshold is updated according to the threshold update configuration file to obtain the updated target detection threshold.

[0012] In one embodiment, the method further comprises:

[0013] Obtaining a current partial discharge monitoring value of the generator to be tested, determining a magnitude relationship between the current partial discharge monitoring value and the target detection threshold; and generating a real-time alarm signal and real-time alarm information when it is identified that the current partial discharge monitoring value exceeds the target detection threshold.

[0014] In one embodiment, obtaining the initial detection threshold includes:

[0015] Obtain historical partial discharge monitoring values and online partial discharge monitoring values of the generator to be tested, generate training samples based on the historical partial discharge monitoring values and the online partial discharge monitoring values; perform training based on the training samples to obtain an initial threshold configuration file, and obtain the initial detection threshold based on the initial threshold configuration file.

[0016] In one embodiment, the performing signal preprocessing on the original partial discharge signal to obtain a target partial discharge signal includes:

[0017] According to a preset cutoff frequency, the original partial discharge signal is subjected to anti-aliasing filtering to obtain a filtered partial discharge signal; the filtered partial discharge signal is amplified according to a target gain, and the amplified partial discharge signal is subjected to analog-to-digital conversion to obtain the target partial discharge signal.

[0018] In one embodiment, the method further comprises:

[0019] generating a feedback signal according to the false alarm rate and the missed alarm rate of the generator stator partial discharge, and driving the stochastic gradient descent algorithm to perform parameter update according to the feedback signal to obtain an updated stochastic gradient descent algorithm;

[0020] The calculating, based on the statistical characteristics, the frequency domain characteristics, and the discharge type, the false alarm rate and the missed alarm rate of the generator stator partial discharge by a stochastic gradient descent algorithm includes:

[0021] The statistical features, the frequency domain features, and the discharge type are input into the updated stochastic gradient descent algorithm to obtain the false alarm rate and the missed alarm rate of the generator stator partial discharge.

[0022] In a second aspect, the present application further provides a device for optimizing a generator stator partial discharge detection threshold, comprising:

[0023] a signal processing module, configured to obtain an original partial discharge signal collected by a partial discharge sensor of the generator to be tested, and perform signal preprocessing on the original partial discharge signal to obtain a target partial discharge signal;

[0024] a feature extraction module, configured to perform feature extraction and pattern recognition on the target partial discharge signal to obtain statistical features, frequency domain features, and discharge type of the generator stator partial discharge;

[0025] a function determination module, configured to calculate the false alarm rate and missed alarm rate of the generator stator partial discharge according to the statistical characteristics, the frequency domain characteristics, and the discharge type by using a stochastic gradient descent algorithm, and determine a comprehensive loss function according to the false alarm rate and the missed alarm rate;

[0026] The threshold optimization module is used to obtain an initial detection threshold, optimize the initial detection threshold according to the learning rate of the stochastic gradient descent algorithm and the comprehensive loss function, and obtain an updated target detection threshold.

[0027] In a third aspect, the present application further provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0028] Acquiring an original partial discharge signal collected by a partial discharge sensor of the generator to be tested, performing signal preprocessing on the original partial discharge signal to obtain a target partial discharge signal;

[0029] Performing feature extraction and pattern recognition on the target partial discharge signal to obtain statistical features, frequency domain features, and discharge type of the generator stator partial discharge;

[0030] Calculating the false alarm rate and missed alarm rate of the generator stator partial discharge using a stochastic gradient descent algorithm based on the statistical characteristics, the frequency domain characteristics, and the discharge type, and determining a comprehensive loss function based on the false alarm rate and the missed alarm rate;

[0031] An initial detection threshold is obtained, and the initial detection threshold is optimized according to the learning rate of the stochastic gradient descent algorithm and the comprehensive loss function to obtain an updated target detection threshold.

[0032] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the following steps are implemented:

[0033] Acquiring an original partial discharge signal collected by a partial discharge sensor of the generator to be tested, performing signal preprocessing on the original partial discharge signal to obtain a target partial discharge signal;

[0034] Performing feature extraction and pattern recognition on the target partial discharge signal to obtain statistical features, frequency domain features, and discharge type of the generator stator partial discharge;

[0035] Calculating the false alarm rate and missed alarm rate of the generator stator partial discharge using a stochastic gradient descent algorithm based on the statistical characteristics, the frequency domain characteristics, and the discharge type, and determining a comprehensive loss function based on the false alarm rate and the missed alarm rate;

[0036] An initial detection threshold is obtained, and the initial detection threshold is optimized according to the learning rate of the stochastic gradient descent algorithm and the comprehensive loss function to obtain an updated target detection threshold.

[0037] In a fifth aspect, the present application further provides a computer program product, comprising a computer program, which, when executed by a processor, implements the following steps:

[0038] Acquiring an original partial discharge signal collected by a partial discharge sensor of the generator to be tested, performing signal preprocessing on the original partial discharge signal to obtain a target partial discharge signal;

[0039] Performing feature extraction and pattern recognition on the target partial discharge signal to obtain statistical features, frequency domain features, and discharge type of the generator stator partial discharge;

[0040] Calculating the false alarm rate and missed alarm rate of the generator stator partial discharge using a stochastic gradient descent algorithm based on the statistical characteristics, the frequency domain characteristics, and the discharge type, and determining a comprehensive loss function based on the false alarm rate and the missed alarm rate;

[0041] An initial detection threshold is obtained, and the initial detection threshold is optimized according to the learning rate of the stochastic gradient descent algorithm and the comprehensive loss function to obtain an updated target detection threshold.

[0042] The above-mentioned optimization method, device, computer equipment, computer-readable storage medium and computer program product for the generator stator partial discharge detection threshold, by extracting features and pattern recognition of the target partial discharge signal, obtains the statistical characteristics, frequency domain characteristics and discharge type of the target partial discharge signal; then, based on the statistical characteristics, frequency domain characteristics and discharge type, the stochastic gradient descent algorithm is used to calculate the false alarm rate and missed alarm rate of the generator stator partial discharge, and a comprehensive loss function is determined based on the false alarm rate and missed alarm rate; then, an initial detection threshold is obtained, and the initial detection threshold is optimized according to the learning rate of the stochastic gradient descent algorithm and the comprehensive loss function to obtain an updated target detection threshold. The present application realizes the adaptive optimization and update of the partial discharge detection threshold through a closed-loop feedback mechanism and a stochastic gradient descent algorithm, which can automatically adjust the detection threshold of the generator stator partial discharge, can cope with real-time changes such as equipment aging and load fluctuations, and greatly shortens the adjustment cycle of the detection threshold, thereby improving the dynamic adaptability of the threshold adjustment and reducing the false alarm rate and missed alarm rate of the generator stator partial discharge. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following briefly introduces the drawings required for use in the embodiments of the present application or related technical descriptions. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying any creative work.

[0044] Figure 1 FIG. 1 is an application environment diagram of a method for optimizing a generator stator partial discharge detection threshold value according to an embodiment;

[0045] Figure 2 1 is a flow chart of a method for optimizing a generator stator partial discharge detection threshold in one embodiment;

[0046] Figure 3 Schematic diagram of a flow chart of a threshold optimization step in one embodiment;

[0047] Figure 4 1 is a flow chart of a method for optimizing a generator stator partial discharge detection threshold in a specific embodiment;

[0048] Figure 5 1. It is a flow chart of a method for optimizing a generator stator partial discharge detection threshold in an application embodiment;

[0049] Figure 6 1 is a structural block diagram of a device for optimizing a generator stator partial discharge detection threshold in one embodiment;

[0050] Figure 7 FIG. 1 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0051] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0052] The optimization method for detecting the threshold value of partial discharge of the stator of the generator provided in the embodiment of the present application can be applied to the following examples: Figure 1 In the application environment shown in FIG. , the terminal communicates with the server through the network. The data storage system can store the data that the server needs to process. The data storage system can be integrated on the server or placed on the cloud or other network servers. Figure 1 In the application environment shown, the terminal can be, but is not limited to, various personal computers, laptops, smart phones, and tablet computers. The server can be implemented as an independent server or a server cluster consisting of multiple servers.

[0053] In one embodiment, Figure 2 As shown in FIG, a method for optimizing the detection threshold of partial discharge of a generator stator is provided. Figure 1 The following steps are used as an example to illustrate the terminal in the figure:

[0054] Step S201 : obtaining an original partial discharge signal collected by a partial discharge sensor of a generator to be tested, performing signal preprocessing on the original partial discharge signal, and obtaining a target partial discharge signal.

[0055] The partial discharge sensor may be a high-frequency pulse current sensor built into the stator winding of the generator, and its sampling rate may be set to 40 MHz-350 MHz.

[0056] Specifically, the terminal is set to collect partial discharge signals every 50ms when the generator under test is running, and obtains the original partial discharge signals collected by the high-frequency pulse current sensor. The original partial discharge signals are preprocessed by signal filtering, signal amplification, and analog-to-digital conversion to obtain the target partial discharge signals.

[0057] Step S202 : performing feature extraction and pattern recognition on the target partial discharge signal to obtain statistical features, frequency domain features and discharge type of the generator stator partial discharge.

[0058] The statistical features include but are not limited to the discharge pulse peak value and pulse repetition rate of the target partial discharge signal, and the frequency domain features may be the spectrum energy distribution of the target partial discharge signal in the range of 50-1000 kHz.

[0059] Specifically, the terminal analyzes the statistical characteristics and frequency domain characteristics of the target partial discharge signal to obtain the discharge pulse peak and pulse repetition rate of the target partial discharge signal, and classifies the target partial discharge signal into discharge types based on wavelet packet decomposition to obtain the discharge type corresponding to the generator stator partial discharge.

[0060] Step S203 , calculating the false alarm rate and missed alarm rate of the generator stator partial discharge according to the statistical characteristics, frequency domain characteristics and discharge type by using a stochastic gradient descent algorithm, and determining a comprehensive loss function according to the false alarm rate and missed alarm rate.

[0061] Among them, the comprehensive loss function can be expressed as (0.7FPR + 0.3FNR), where FPR is the false alarm rate and FNR is the false negative rate.

[0062] Specifically, the terminal uses an improved stochastic gradient descent algorithm to calculate the false alarm rate and missed alarm rate of generator stator partial discharge based on statistical characteristics, frequency domain characteristics and discharge type, and determines the comprehensive loss function based on the false alarm rate and missed alarm rate.

[0063] Step S204: Obtain an initial detection threshold, optimize the initial detection threshold according to the learning rate of the stochastic gradient descent algorithm and the comprehensive loss function, and obtain an updated target detection threshold.

[0064] Among them, the stochastic gradient descent algorithm (SGD) is an efficient optimization algorithm that can be applied to machine learning and deep learning; the learning rate of the stochastic gradient descent algorithm is a learning rate that decays over time and is used to control the step size of parameter updates.

[0065] Specifically, the terminal dynamically generates training samples based on online monitoring data and historical databases, and uses the training samples for training to generate an initial detection threshold. Then, based on the learning rate of the stochastic gradient descent algorithm and the comprehensive loss function, the initial detection threshold is optimized through a preset threshold optimization model to obtain an updated target detection threshold.

[0066] In the above-mentioned optimization method for the detection threshold of partial discharge of the generator stator, the statistical characteristics, frequency domain characteristics and discharge type of the target partial discharge signal of the target partial discharge are obtained by performing feature extraction and pattern recognition on the target partial discharge signal; then, based on the statistical characteristics, frequency domain characteristics and discharge type, the false alarm rate and missed alarm rate of the partial discharge of the generator stator are calculated by the random gradient descent algorithm, and the comprehensive loss function is determined based on the false alarm rate and missed alarm rate; then, the initial detection threshold is obtained, and the initial detection threshold is optimized according to the learning rate of the random gradient descent algorithm and the comprehensive loss function to obtain the updated target detection threshold. The present application realizes the adaptive optimization of the partial discharge detection threshold through a closed-loop feedback mechanism and a random gradient descent algorithm, which can automatically adjust the detection threshold of the partial discharge of the generator stator, and can cope with real-time changes such as equipment aging and load fluctuations. At the same time, it also greatly shortens the adjustment cycle of the detection threshold, thereby improving the dynamic adaptability of the threshold adjustment and reducing the false alarm rate and missed alarm rate of the partial discharge of the generator stator.

[0067] In one embodiment, Figure 3 As shown, in the above step S204, the initial detection threshold is optimized according to the learning rate and the comprehensive loss function of the stochastic gradient descent algorithm to obtain the updated target detection threshold, which specifically includes the following steps:

[0068] In step S301, the initial detection threshold, the learning rate of the stochastic gradient descent algorithm, and the comprehensive loss function are input into a preset threshold optimization model to perform threshold optimization calculation to obtain a threshold target value output by the threshold optimization model.

[0069] Step S302: Generate a threshold update configuration file based on the threshold target amount.

[0070] Step S303 : In response to the threshold update instruction sent by the server, the initial detection threshold is updated according to the threshold update configuration file to obtain an updated target detection threshold.

[0071] Among them, the preset threshold optimization model can be , in the above formula is the initial detection threshold (unit: pC (picocoulombs)), is the threshold target (unit: pC), η(t) is the learning rate that decays over time, and J is the comprehensive loss function (0.7FPR + 0.3FNR).

[0072] Specifically, the terminal determines the learning rate of the stochastic gradient descent algorithm, inputs the initial detection threshold, the learning rate of the stochastic gradient descent algorithm, and the comprehensive loss function into the preset threshold optimization model for threshold optimization calculation, and obtains the threshold target amount output by the threshold optimization model; based on the threshold target amount, generates a threshold update configuration file in JSON format; in response to the threshold update instruction sent by the server, updates the initial detection threshold according to the threshold update configuration file to obtain an updated target detection threshold.

[0073] In one embodiment, the method of the present application further includes:

[0074] The current partial discharge monitoring value of the generator under test is obtained, and the relationship between the current partial discharge monitoring value and the target detection threshold is determined; when it is identified that the current partial discharge monitoring value exceeds the target detection threshold, a real-time alarm signal and real-time alarm information are generated.

[0075] The real-time alarm signal may be in the form of a light signal alarm or an acoustic signal alarm.

[0076] Specifically, the terminal obtains the current partial discharge monitoring value of the generator under test in real time, determines the size relationship between the current partial discharge monitoring value and the target detection threshold; and generates a real-time alarm signal and real-time alarm information when it is identified that the current partial discharge monitoring value exceeds the target detection threshold.

[0077] In one embodiment, in step S204, obtaining the initial detection threshold specifically includes the following steps:

[0078] The historical partial discharge monitoring values and online partial discharge monitoring values of the generator under test are obtained, and training samples are generated based on the historical partial discharge monitoring values and the online partial discharge monitoring values. An initial threshold configuration file is obtained through training based on the training samples, and an initial detection threshold is obtained based on the initial threshold configuration file.

[0079] Specifically, the terminal obtains the historical partial discharge monitoring values and online partial discharge monitoring values of the generator to be tested, and dynamically generates training samples based on the historical partial discharge monitoring values and the online partial discharge monitoring values; trains based on the training samples to obtain an initial threshold configuration file, and generates an initial detection threshold based on the initial threshold configuration file.

[0080] In one embodiment, in step S201, the original partial discharge signal is preprocessed to obtain a target partial discharge signal, which specifically includes the following steps:

[0081] According to a preset cutoff frequency, the original partial discharge signal is subjected to anti-aliasing filtering to obtain a filtered partial discharge signal; the filtered partial discharge signal is amplified according to a target gain, and the amplified partial discharge signal is subjected to analog-to-digital conversion to obtain a target partial discharge signal.

[0082] The preset cutoff frequency can be set to 4 MHz, the gain of the signal amplification can be set to 20 dB, and analog-to-digital conversion can be performed through a 16-bit ADC.

[0083] Specifically, the terminal performs anti-aliasing filtering on the original partial discharge signal according to a preset cutoff frequency to obtain a filtered partial discharge signal; then amplifies the filtered partial discharge signal according to a target gain, and performs analog-to-digital conversion on the amplified partial discharge signal to obtain a target partial discharge signal.

[0084] In one embodiment, the method of the present application further includes:

[0085] Generate a feedback signal based on the false alarm rate and missed alarm rate of the generator stator partial discharge, and drive the stochastic gradient descent algorithm to update the parameters based on the feedback signal to obtain an updated stochastic gradient descent algorithm;

[0086] In the above step S203, the false alarm rate and missed alarm rate of the generator stator partial discharge are calculated based on the statistical characteristics, frequency domain characteristics and discharge type by using the stochastic gradient descent algorithm, which specifically includes the following steps:

[0087] The statistical features, frequency domain features and discharge types are input into the updated stochastic gradient descent algorithm to obtain the false alarm rate and missed alarm rate of partial discharge in the generator stator.

[0088] Specifically, the terminal generates a false alarm / missing alarm result based on the false alarm rate and missing alarm rate of the generator stator partial discharge, uses the false alarm / missing alarm result as a feedback signal, and drives the stochastic gradient descent algorithm to update the parameters according to the feedback signal to obtain an updated stochastic gradient descent algorithm; the statistical features, frequency domain features and discharge type are input into the updated stochastic gradient descent algorithm to obtain the false alarm rate and missing alarm rate of the generator stator partial discharge.

[0089] In one embodiment, Figure 4 As shown, a method for optimizing the generator stator partial discharge detection threshold in a specific embodiment is provided, which specifically includes the following steps:

[0090] Step S401: Obtain a raw partial discharge signal collected by a partial discharge sensor of a generator under test, perform anti-aliasing filtering on the raw partial discharge signal according to a preset cutoff frequency to obtain a filtered partial discharge signal; amplify the filtered partial discharge signal according to a target gain, and perform analog-to-digital conversion on the amplified partial discharge signal to obtain a target partial discharge signal.

[0091] Step S402 : performing feature extraction and pattern recognition on the target partial discharge signal to obtain statistical features, frequency domain features and discharge type of the generator stator partial discharge.

[0092] Step S403 , calculating the false alarm rate and missed alarm rate of the generator stator partial discharge according to the statistical characteristics, frequency domain characteristics and discharge type by using a stochastic gradient descent algorithm, and determining a comprehensive loss function according to the false alarm rate and missed alarm rate.

[0093] Step S404: Obtain historical partial discharge monitoring values and online partial discharge monitoring values of the generator to be tested, generate training samples based on the historical partial discharge monitoring values and the online partial discharge monitoring values; perform training based on the training samples to obtain an initial threshold configuration file, and obtain an initial detection threshold based on the initial threshold configuration file.

[0094] In step S405, the initial detection threshold, the learning rate of the stochastic gradient descent algorithm, and the comprehensive loss function are input into a preset threshold optimization model to perform threshold optimization calculations to obtain a threshold target value output by the threshold optimization model; based on the threshold target value, a threshold update configuration file is generated; in response to the threshold update instruction sent by the server, the initial detection threshold is updated according to the threshold update configuration file to obtain an updated target detection threshold.

[0095] Step S406, obtaining the current partial discharge monitoring value of the generator to be tested, determining the magnitude relationship between the current partial discharge monitoring value and the target detection threshold; generating a real-time alarm signal and real-time alarm information when it is identified that the current partial discharge monitoring value exceeds the target detection threshold.

[0096] The beneficial effects brought about by the above embodiment are as follows:

[0097] This application realizes adaptive optimization and update of the partial discharge detection threshold through a closed-loop feedback mechanism and a stochastic gradient descent algorithm. It can automatically adjust the detection threshold of partial discharge of the generator stator, and can cope with real-time changes such as equipment aging and load fluctuations. At the same time, it also greatly shortens the adjustment cycle of the detection threshold, thereby improving the dynamic adaptability of the threshold adjustment and reducing the false alarm rate and missed alarm rate of partial discharge of the generator stator.

[0098] In order to more clearly illustrate the optimization method of the generator stator partial discharge detection threshold provided by the embodiment of the present application, the optimization method of the generator stator partial discharge detection threshold is specifically described below using a specific embodiment. As an application embodiment, Figure 5 As shown, another method for optimizing the detection threshold of partial discharge of a generator stator is provided, which specifically includes the following steps:

[0099] Step 1, initialization phase (T0):

[0100] (1) The server loads historical partial discharge data to train the initial threshold (the default threshold is 100pC);

[0101] (2) The edge computing terminal obtains the initial threshold configuration.

[0102] Step 2: Real-time operation phase (T1-Tn):

[0103] Run the sequence:

[0104] Edge computing terminal->server: send feature vector (every 10 seconds);

[0105] Server->Server: Online learning and computing;

[0106] Server->Edge computing terminal: returns the updated target detection threshold;

[0107] Edge computing terminal->executor: update detection threshold;

[0108] Actuator->Sensor: Continue monitoring.

[0109] Step 3: Feedback optimization cycle:

[0110] Data acquisition frequency: 10MHz (configurable);

[0111] Threshold update frequency: 1Hz (configurable).

[0112] The beneficial effects brought about by the above embodiment are as follows:

[0113] 1) Improved dynamic adaptability: The threshold adjustment cycle is shortened from "days" to "minutes", and the false alarm rate is reduced by 30%.

[0114] 2) Reduced operation and maintenance costs: Eliminating manual intervention, and model self-evolution saves 90% of maintenance hours.

[0115] 3) Enhanced long-term stability: Through online learning, data distribution shift is resisted, and the model accuracy decay rate is reduced to 2% / year.

[0116] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily executed and completed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0117] Based on the same inventive concept, embodiments of the present application further provide a generator stator partial discharge detection threshold optimization device for implementing the aforementioned generator stator partial discharge detection threshold optimization method. The solution provided by this device is similar to the solution described in the aforementioned method. Therefore, the specific limitations of one or more embodiments of the generator stator partial discharge detection threshold optimization device provided below can be found in the above-described limitations of the generator stator partial discharge detection threshold optimization method and are not further elaborated here.

[0118] In an exemplary embodiment, Figure 6 As shown, a device for optimizing the detection threshold of partial discharge of a generator stator is provided, comprising:

[0119] The signal processing module 601 is used to obtain the original partial discharge signal collected by the partial discharge sensor of the generator to be tested, perform signal preprocessing on the original partial discharge signal, and obtain the target partial discharge signal;

[0120] A feature extraction module 602 is used to perform feature extraction and pattern recognition on the target partial discharge signal to obtain statistical features, frequency domain features and discharge type of the generator stator partial discharge;

[0121] Function determination module 603, configured to calculate the false alarm rate and missed alarm rate of the generator stator partial discharge based on the statistical characteristics, frequency domain characteristics, and discharge type using a stochastic gradient descent algorithm, and determine a comprehensive loss function based on the false alarm rate and missed alarm rate;

[0122] The threshold optimization module 604 is used to obtain an initial detection threshold, optimize the initial detection threshold according to the learning rate of the stochastic gradient descent algorithm and the comprehensive loss function, and obtain an updated target detection threshold.

[0123] In one embodiment, the threshold optimization module 604 is also used to input the initial detection threshold, the learning rate of the stochastic gradient descent algorithm, and the comprehensive loss function into a preset threshold optimization model to perform threshold optimization calculations to obtain the threshold target quantity output by the threshold optimization model; generate a threshold update configuration file based on the threshold target quantity; and update the initial detection threshold according to the threshold update configuration file in response to the threshold update instruction sent by the server to obtain an updated target detection threshold.

[0124] In one embodiment, the device for optimizing the generator stator partial discharge detection threshold further includes a real-time alarm module for obtaining a current partial discharge monitoring value of the generator to be tested, determining a magnitude relationship between the current partial discharge monitoring value and a target detection threshold, and generating a real-time alarm signal and real-time alarm information when it is determined that the current partial discharge monitoring value exceeds the target detection threshold.

[0125] In one embodiment, the threshold optimization module 604 is further configured to obtain historical partial discharge monitoring values and online partial discharge monitoring values of the generator under test, generate training samples based on the historical partial discharge monitoring values and the online partial discharge monitoring values, perform training based on the training samples to obtain an initial threshold configuration file, and obtain an initial detection threshold based on the initial threshold configuration file.

[0126] In one embodiment, the signal processing module 601 is further configured to perform anti-aliasing filtering on the original partial discharge signal according to a preset cutoff frequency to obtain a filtered partial discharge signal; amplify the filtered partial discharge signal according to a target gain; and perform analog-to-digital conversion on the amplified partial discharge signal to obtain a target partial discharge signal.

[0127] In one embodiment, the device for optimizing the generator stator partial discharge detection threshold further includes an algorithm updating module for generating a feedback signal based on the false alarm rate and missed alarm rate of the generator stator partial discharge, and driving the stochastic gradient descent algorithm to perform parameter updates based on the feedback signal to obtain an updated stochastic gradient descent algorithm; and a function determination module 603 for inputting statistical features, frequency domain features, and discharge type into the updated stochastic gradient descent algorithm to obtain the false alarm rate and missed alarm rate of the generator stator partial discharge.

[0128] Each module in the aforementioned generator stator partial discharge detection threshold optimization device can be implemented in whole or in part via software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in hardware form, or stored in a computer device memory in software form, so that the processor can call and execute the corresponding operations of each module.

[0129] In an exemplary embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as shown in FIG. Figure 7 As shown. The computer device includes a processor, memory, an input / output interface, a communication interface, a display unit, and an input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are connected to the system bus via the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals via wired or wireless means, and the wireless means can be implemented via Wi-Fi, mobile cellular networks, near-field communication (NFC), or other technologies. When executed by the processor, the computer program implements a method for optimizing the detection threshold of partial discharge in a generator stator. The display unit of the computer device is used to form a visually visible image and can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device casing, or an external keyboard, touchpad or mouse.

[0130] Those skilled in the art will understand that Figure 7 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0131] In one embodiment, a computer device is further provided, including a memory and a processor. The memory stores a computer program, and the processor implements the steps in the above method embodiments when executing the computer program.

[0132] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0133] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0134] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0135] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. In particular, any reference to memory, database, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the various embodiments provided herein may be, but are not limited to, general-purpose processors, central processing units (CPUs), graphics processing units (GPUs), digital signal processors (DSPs), programmable logic devices (PLDs), quantum computing-based data processing logic devices, artificial intelligence (AI) processors, and the like.

[0136] The technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0137] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A method for optimizing the detection threshold of partial discharge of a generator stator, characterized in that: The method comprises: Acquiring an original partial discharge signal collected by a partial discharge sensor of the generator to be tested, performing signal preprocessing on the original partial discharge signal to obtain a target partial discharge signal; Performing feature extraction and pattern recognition on the target partial discharge signal to obtain statistical features, frequency domain features, and discharge type of the generator stator partial discharge; Calculating the false alarm rate and missed alarm rate of the generator stator partial discharge using a stochastic gradient descent algorithm based on the statistical characteristics, the frequency domain characteristics, and the discharge type, and determining a comprehensive loss function based on the false alarm rate and the missed alarm rate; An initial detection threshold is obtained, and the initial detection threshold is optimized according to the learning rate of the stochastic gradient descent algorithm and the comprehensive loss function to obtain an updated target detection threshold.

2. The method according to claim 1, characterized in that Optimizing the initial detection threshold according to the learning rate of the stochastic gradient descent algorithm and the comprehensive loss function to obtain an updated target detection threshold includes: Inputting the initial detection threshold, the learning rate of the stochastic gradient descent algorithm, and the comprehensive loss function into a preset threshold optimization model to perform threshold optimization calculation, thereby obtaining a threshold target value output by the threshold optimization model; generating a threshold update configuration file according to the threshold target amount; In response to the threshold update instruction sent by the server, the initial detection threshold is updated according to the threshold update configuration file to obtain the updated target detection threshold.

3. The method according to claim 2, characterized in that The method further comprises: Obtaining a current partial discharge monitoring value of the generator to be tested, and determining a magnitude relationship between the current partial discharge monitoring value and the target detection threshold; When it is identified that the current partial discharge monitoring value exceeds the target detection threshold, a real-time alarm signal and real-time alarm information are generated.

4. The method according to claim 1, wherein The obtaining of the initial detection threshold comprises: Obtaining historical partial discharge monitoring values and online partial discharge monitoring values of the generator to be tested, and generating training samples based on the historical partial discharge monitoring values and the online partial discharge monitoring values; An initial threshold configuration file is obtained by training based on the training samples, and the initial detection threshold is obtained according to the initial threshold configuration file.

5. The method according to claim 1, wherein The performing signal preprocessing on the original partial discharge signal to obtain a target partial discharge signal includes: performing anti-aliasing filtering on the original partial discharge signal according to a preset cutoff frequency to obtain a filtered partial discharge signal; The filtered partial discharge signal is amplified according to a target gain, and the amplified partial discharge signal is converted into analog to digital form to obtain the target partial discharge signal.

6. The method according to any one of claims 1 to 5, characterized in that The method further comprises: generating a feedback signal according to the false alarm rate and the missed alarm rate of the generator stator partial discharge, and driving the stochastic gradient descent algorithm to perform parameter update according to the feedback signal to obtain an updated stochastic gradient descent algorithm; The calculating, based on the statistical characteristics, the frequency domain characteristics, and the discharge type, the false alarm rate and the missed alarm rate of the generator stator partial discharge by a stochastic gradient descent algorithm includes: The statistical features, the frequency domain features, and the discharge type are input into the updated stochastic gradient descent algorithm to obtain the false alarm rate and the missed alarm rate of the generator stator partial discharge.

7. A device for optimizing the detection threshold of partial discharge of a generator stator, characterized in that: The device comprises: a signal processing module, configured to obtain an original partial discharge signal collected by a partial discharge sensor of the generator to be tested, and perform signal preprocessing on the original partial discharge signal to obtain a target partial discharge signal; a feature extraction module, configured to perform feature extraction and pattern recognition on the target partial discharge signal to obtain statistical features, frequency domain features, and discharge type of the generator stator partial discharge; a function determination module, configured to calculate the false alarm rate and missed alarm rate of the generator stator partial discharge according to the statistical characteristics, the frequency domain characteristics, and the discharge type by using a stochastic gradient descent algorithm, and determine a comprehensive loss function according to the false alarm rate and the missed alarm rate; The threshold optimization module is used to obtain an initial detection threshold, optimize the initial detection threshold according to the learning rate of the stochastic gradient descent algorithm and the comprehensive loss function, and obtain an updated target detection threshold.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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