Partial discharge identification method and device of power equipment, computer readable storage medium and electronic device

Abstract

The application discloses a partial discharge identification method and device of a power equipment, a computer readable storage medium and an electronic device. The method comprises the following steps: acquiring a partial discharge pulse signal of the power equipment within a first preset time window; determining at least one statistical parameter of the partial discharge pulse signal based on the partial discharge pulse signal; determining a statistical significance index of the partial discharge pulse signal based on the at least one statistical parameter; calling a target identification model to detect the partial discharge pulse signal based on a comparison result of the statistical significance index and a statistical significance index threshold, and obtaining a partial discharge identification result of the power equipment, wherein the partial discharge identification result at least comprises a discharge type of the partial discharge of the power equipment and a confidence degree of the partial discharge identification result, and the confidence degree is used for evaluating the accuracy of the partial discharge identification result. The application solves the technical problem of inaccurate partial discharge identification of the power equipment.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electric power, in particular to a partial discharge identification method and device of electric power equipment, a computer readable storage medium and an electronic device. BACKGROUND

[0002] At present, due to various uncontrollable factors, there may be insulation defects in the design, manufacture, transportation and installation process of electric power equipment, especially high-voltage electrical equipment such as gas insulated switchgear (GIS). These defects are manifested as partial discharge (PD) phenomena in the operation of electric power equipment, which is an early signal of electrical equipment aging and potential failure. Partial discharge not only accelerates the deterioration of insulation materials, but also may cause more serious equipment failure, which threatens the safety and stability of the power system. Therefore, accurately and timely identifying partial discharge and judging whether it is related to insulation defects is the key to preventing electric power equipment failure and ensuring the smooth operation of the power system.

[0003] In related technologies, partial discharge identification mainly relies on two technical means: phase-resolved partial discharge (PRPD) image analysis and phase-resolved pulse sequence (PRPS) image analysis. Among them, the PRPD image can intuitively show the relationship between the discharge pulse and the voltage phase, while the PRPS image emphasizes the periodicity and repeatability of the discharge pulse. However, these identification methods are easily affected by the mixing of partial discharge signals and external interference signals, resulting in a decrease in identification accuracy and a poor application effect. In addition, due to the intermittent and statistical characteristics of partial discharge caused by many initial defects, traditional identification algorithms are difficult to make accurate judgments within a limited time or period. Moreover, the PRPD image analysis method and the PRPS image analysis method often require professionals to interpret the PRPD and PRPS images based on experience and professional knowledge, which is not only inefficient, but also easily affected by subjective judgment, and there is a technical problem of inaccurate identification of partial discharge of electric power equipment.

[0004] In view of the above technical problem of inaccurate identification of partial discharge of electric power equipment, no effective solution has been proposed so far. SUMMARY

[0005] The embodiment of the present application provides a partial discharge identification method and device of a power equipment, a computer readable storage medium and an electronic device, so as to at least solve the technical problem of inaccurate partial discharge identification of the power equipment.

[0006] According to an aspect of the embodiment of the present application, a partial discharge identification method of a power equipment is provided, which comprises: acquiring a partial discharge pulse signal of the power equipment within a first preset time window, wherein the partial discharge pulse signal is used to represent discharge characteristics of the power equipment within the first preset time window; determining at least one statistical parameter of the partial discharge pulse signal based on the partial discharge pulse signal, wherein the statistical parameter is used to represent distribution characteristics of the partial discharge pulse signal; determining a statistical significance index of the partial discharge pulse signal based on the at least one statistical parameter, wherein the statistical significance index is used to quantify the degree of significance of the partial discharge pulse relative to background noise, and the background noise is used to indicate an interference signal of the partial discharge pulse signal; calling a target identification model to detect the partial discharge pulse signal based on a comparison result of the statistical significance index and a statistical significance index threshold value, to obtain a partial discharge identification result of the power equipment, wherein the target identification model comprises a phase-based statistical identification model and a time sequence analysis model, and the partial discharge identification result at least comprises a discharge type of the partial discharge of the power equipment and a confidence degree of the partial discharge identification result, and the confidence degree is used to evaluate the accuracy of the partial discharge identification result.

[0007] Optionally, the determination of the at least one statistical parameter of the partial discharge pulse signal based on the partial discharge pulse signal comprises: pre-processing the partial discharge pulse signal to obtain a pre-processed partial discharge pulse signal, wherein the pre-processing is used to filter out high-frequency noise and low-frequency noise in the partial discharge pulse signal; and determining the at least one statistical parameter of the pre-processed partial discharge pulse signal based on the pre-processed partial discharge pulse signal, wherein the statistical parameter at least comprises one of the following: a signal amplitude, a signal frequency and a signal duration of the pre-processed partial discharge pulse signal.

[0008] Optionally, the determination of the statistical significance index of the partial discharge pulse signal based on the at least one statistical parameter comprises: performing standardization processing on the at least one statistical parameter to obtain a standardized value of the at least one statistical parameter; and performing weighted summation on the standardized value of the at least one statistical parameter to obtain the statistical significance index.

[0009] Optionally, the statistical significance index is obtained by weighted sum of the standardized values of the at least one statistical parameter, comprising: determining the weight corresponding to the standardized value of each statistical parameter, wherein the weight is used to represent the influence degree of the statistical parameter on the statistical significance of the partial discharge pulse signal, and the weight is adjusted at least according to the discharge characteristics of the partial discharge pulse signal and the partial discharge pulse signal of the power equipment in the historical stage; and obtaining the statistical significance index by weighted sum of the standardized values of the at least one statistical parameter and the weight corresponding to the standardized values.

[0010] Optionally, based on the comparison result of the statistical significance index and the statistical significance index threshold, a target recognition model is called to detect the partial discharge pulse signal to obtain the partial discharge recognition result of the power equipment, comprising: in response to the comparison result indicating that the statistical significance index is greater than the statistical significance index threshold, calling a phase-based statistical recognition model to detect the partial discharge pulse signal to obtain the first partial discharge recognition result of the power equipment; and in response to the comparison result indicating that the statistical significance index is not greater than the statistical significance index threshold, calling a time series analysis model to detect the partial discharge pulse signal to obtain the second partial discharge recognition result of the power equipment.

[0011] Optionally, after obtaining the second partial discharge recognition result, the partial discharge recognition method of the power equipment further comprises: obtaining the partial discharge pulse signal of the power equipment within a second preset time length, wherein the second preset time length is greater than the first preset time length; determining the statistical significance index corresponding to the partial discharge pulse signal obtained within the second preset time length; in response to the statistical significance index corresponding to the partial discharge pulse signal obtained within the second preset time length being greater than the statistical significance index threshold, calling the phase-based statistical recognition model to detect the partial discharge pulse signal of the power equipment obtained within the second preset time length to obtain the third partial discharge recognition result of the power equipment; and updating the second partial discharge recognition result by using the third partial discharge recognition result.

[0012] Optionally, the discharge type is at least one of the following: corona discharge, surface discharge and floating discharge.

[0013] According to another aspect of the embodiments of the present application, there is also provided a partial discharge identification device of a power device. The device comprises: an acquisition unit configured to acquire a partial discharge pulse signal of the power device within a first preset time window, wherein the partial discharge pulse signal is configured to represent a discharge feature of the power device within the first preset time window; a first determination unit configured to determine at least one statistical parameter of the partial discharge pulse signal based on the partial discharge pulse signal, wherein the statistical parameter is configured to represent a distribution feature of the partial discharge pulse signal; a second determination unit configured to determine a statistical significance index of the partial discharge pulse signal based on the at least one statistical parameter, wherein the statistical significance index is configured to quantify a significant degree of the partial discharge pulse relative to background noise, and the background noise is configured to indicate an interference signal of the partial discharge pulse signal; and a calling unit configured to call a target identification model to detect the partial discharge pulse signal based on a comparison result of the statistical significance index and a statistical significance index threshold, and obtain a partial discharge identification result of the power device, wherein the target identification model comprises a phase-based statistical identification model and a time series analysis model, and the partial discharge identification result at least comprises a discharge type of the partial discharge of the power device and a confidence degree of the partial discharge identification result, and the confidence degree is configured to evaluate an accuracy degree of the partial discharge identification result.

[0014] According to another aspect of the embodiments of the present application, there is also provided a computer readable storage medium comprising a stored program, wherein the program, when executed by a processor, controls a device where the storage medium is located to perform the partial discharge identification method of the power device in the embodiments of the present application.

[0015] According to another aspect of the embodiments of the present application, there is also provided a processor configured to execute a program, wherein the program, when executed, performs the partial discharge identification method of the power device in the embodiments of the present application.

[0016] According to another aspect of the embodiments of the present application, there is also provided a computer program product comprising computer instructions configured to, when executed by a processor, implement the partial discharge identification method of the power device in the embodiments of the present application.

[0017] In the embodiment of the present application, a statistical significance determination mechanism is introduced, which can perform statistical analysis on the partial discharge pulse signal of the power equipment, obtain a statistical significance index of the partial discharge pulse signal of the power equipment, effectively screen out discharge events with significant, and then compare the statistical significance index with a statistical significance index threshold value, dynamically select a target recognition model according to the comparison result, so that the recognition process is more flexible and adaptive, which is helpful to adapt to the recognition requirements under different discharge types and different working conditions, can improve the accuracy of the partial discharge recognition result of the power equipment, and the recognition result carries a confidence degree, which is helpful to reflect the determination degree of the target recognition model to the partial discharge recognition result, and is helpful to the on-site personnel or system to consider the reliability of the partial discharge recognition result when making a decision, thereby solving the technical problem of inaccurate partial discharge recognition of the power equipment. BRIEF DESCRIPTION OF DRAWINGS

[0018] The accompanying drawings, which are included to provide a further understanding of the present application and are incorporated in and constitute a part of this application, illustrate embodiments of the present application and serve to explain the present application. In the drawings:

[0019] Figure 1 is a flowchart of a partial discharge recognition method of a power equipment according to an embodiment of the present application;

[0020] Figure 2 is a schematic diagram of corona discharge according to an embodiment of the present application;

[0021] Figure 3 is a schematic diagram of surface discharge according to an embodiment of the present application;

[0022] Figure 4 is a schematic diagram of suspension discharge according to an embodiment of the present application;

[0023] Figure 5 is a schematic diagram of a model framework of a phase base feature recognition model according to an embodiment of the present application;

[0024] Figure 6 is a schematic diagram of a confusion matrix of a phase base feature recognition model on a test set according to an embodiment of the present application;

[0025] Figure 7 is a schematic diagram of a model framework of a time sequence recognition model according to an embodiment of the present application;

[0026] Figure 8 is a schematic diagram of a confusion matrix of a time sequence recognition model on a test set according to an embodiment of the present application;

[0027] Figure 9is a schematic view of a partial discharge identification device of a power equipment according to an embodiment of the present application. DETAILED DESCRIPTION

[0028] In order to make the personnel in the art better understand the present application, the technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work should belong to the scope of protection of the present application.

[0029] It should be noted that the terms "first", "second" and the like in the specification and claims of the present application and the above-described drawings are used to distinguish similar objects, and do not necessarily indicate a specific order or a chronological sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in other than the order illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, functional component or device including a series of steps or units does not have to be limited to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, functional components or devices.

[0030] Embodiment 1

[0031] According to an embodiment of the present application, an embodiment of a partial discharge identification method of a power equipment is provided. It should be noted that the steps shown in the flowchart of the drawings can be executed in a computer system such as a set of computer executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described herein can be executed in a different order from that shown herein.

[0032] Figure 1 is a flowchart of a partial discharge identification method of a power equipment according to an embodiment of the present application, as shown in Figure 1 The method can include the following steps:

[0033] Step S101, acquiring the partial discharge pulse signal of the power equipment in the first preset time window.

[0034] In the technical solution provided in the above step S101 of the present application, the power equipment can be a high-voltage power equipment in a power system, for example, a gas insulated switchgear (GIS). The first preset time window is used to indicate a selected continuous time range, for example, a time period of 200 milliseconds (ms), 500 milliseconds (ms) or longer, which is not limited here. The partial discharge pulse signal is used to represent the discharge characteristics of the power equipment within the first preset time window.

[0035] In this embodiment, the partial discharge pulse signal of the power equipment within the first preset time window is acquired, and the purpose is to provide original data for subsequent statistical significance analysis and model identification by monitoring and recording the signal generated by the partial discharge phenomenon in the initial stage of the equipment operation.

[0036] In step S102, at least one statistical parameter of the partial discharge pulse signal is determined based on the partial discharge pulse signal.

[0037] In the technical solution provided in the above step S102 of the present application, the statistical parameter is used to represent the distribution characteristics of the partial discharge pulse signal, and the statistical parameter can include but is not limited to the amplitude of the discharge pulse, the frequency of the discharge pulse, the duration of the discharge pulse, etc. The statistical parameter is a numerical value extracted from the partial discharge pulse signal and is used to describe the distribution characteristics and statistical properties of the signal, such as the average amplitude, frequency and duration of the pulse.

[0038] In this embodiment, after the partial discharge pulse signal of the power equipment is acquired, at least one statistical parameter can be extracted from the partial discharge pulse signal.

[0039] In this step, the statistical parameter of the partial discharge pulse signal is determined, which is a process of converting the original partial discharge pulse signal into a quantifiable index. These statistical parameters can reveal the distribution characteristics of the discharge pulse signal and provide key information for subsequent significance judgment and accurate identification of insulation defects in the GIS equipment.

[0040] In step S103, a statistical significance index of the partial discharge pulse signal is determined based on the at least one statistical parameter.

[0041] In the technical solution provided in the above step S103 of the present application, the statistical significance index is used to quantify the degree of significance of the partial discharge pulse relative to the background noise, and the background noise is used to indicate the interference signal of the partial discharge pulse signal.

[0042] In this embodiment, the statistical significance index of the partial discharge pulse signal can be obtained by calculating the obtained at least one statistical parameter through a mathematical formula or an algorithm. Since various electronic noise, electromagnetic interference, etc. exist in the operating environment of the power equipment, these may be misidentified as partial discharge pulse signals by the monitoring system. The background noise refers to the statistical characteristics of the interference signals received by the monitoring system when there is no partial discharge activity. By calculating the statistical significance index, a numerical value can be obtained to quantify the degree of significance of the discharge pulse signal relative to the background noise. The standardized value of each statistical parameter can be calculated by the Z-score method, and then the weighted average calculation is performed on the calculated standardized values according to the weight corresponding to the statistical parameter, so that the statistical significance index of the partial discharge pulse signal can be obtained.

[0043] Optionally, the statistical significance index calculated based on the statistical parameters of the partial discharge pulse signal can quantify the difference between the partial discharge pulse signal and the background noise, help to determine which signals are statistically significant partial discharge signals, and thus provide a basis for subsequent partial discharge identification, reduce misjudgment and improve identification accuracy.

[0044] In step S104, based on the comparison result of the statistical significance index and the statistical significance index threshold, a target recognition model is called to detect the partial discharge pulse signal, and a partial discharge recognition result of the power equipment is obtained.

[0045] In the technical solution provided by the above step S104 of the present application, the target recognition model includes a phase-based statistical recognition model and a time series analysis model, and the partial discharge recognition result at least includes a discharge type of the partial discharge of the power equipment and a confidence degree of the partial discharge recognition result, wherein the discharge type at least includes corona discharge, surface discharge and floating discharge, and the confidence degree is used to evaluate the accuracy of the partial discharge recognition result.

[0046] In this embodiment, after obtaining the statistical significance index, the statistical significance index can be compared with the statistical significance index threshold to obtain a comparison result, and then the target recognition model to be called is determined according to the comparison result. When the statistical significance index exceeds the statistical significance index threshold, it indicates that the partial discharge pulse signal is statistically significantly different from the background noise and has the characteristics of abnormal discharge activity.

[0047] Optionally, if the comparison result of the statistical significance indicator and the statistical significance indicator threshold value indicates that the partial discharge pulse signal has statistical significance, a special model can be called to further analyze and identify the partial discharge pulse signal. Among them, the target recognition model can be a phase-based statistical recognition model or a time series analysis model, wherein the phase-based statistical recognition model is a convolutional neural network (CNN) algorithm based on PRPD spectrum, and the time series analysis model is an algorithm based on a long short-term memory (LSTM) neural network model, depending on the level of statistical significance indicator and the characteristics of the signal. The phase-based statistical recognition model or the time series analysis model can extract complex feature patterns from the partial discharge pulse signal based on advanced technologies such as deep learning, and identify specific discharge types.

[0048] Optionally, through the detection of the target recognition model, a specific partial discharge recognition result can be generated, indicating whether there is a partial discharge phenomenon in the power equipment, and the possible type and location of the discharge. In addition, the partial discharge recognition result can also carry a confidence probability related to the partial discharge recognition result. The confidence probability reflects the degree of certainty of the target recognition model for the partial discharge recognition result, which helps the on-site personnel or system to consider the reliability of the partial discharge recognition result when making decisions. Moreover, through automatic processing and analysis by the model, the influence of manual intervention and subjective judgment is reduced. This helps to improve the efficiency and accuracy of identification and reduce operation and maintenance costs.

[0049] Optionally, after obtaining the partial discharge recognition result, the specific location of the insulation defect in the power equipment can be determined according to the discharge type and discharge location in the partial discharge recognition result, which helps the power equipment maintenance personnel to discover and locate the insulation defect in the early stage, take preventive measures, and avoid the occurrence of faults, thereby ensuring the safe and stable operation of the power system.

[0050] In steps S101-S104, the statistical significance determination mechanism is introduced, which can statistically analyze the partial discharge pulse signal of the power equipment, obtain the statistical significance index of the partial discharge pulse signal of the power equipment, effectively screen out the discharge events with significant, and then compare the statistical significance index with the statistical significance index threshold, and dynamically select the target recognition model according to the comparison result, so that the recognition process is more flexible and adaptive, which is helpful to adapt to the recognition needs under different discharge types and different working conditions, and can improve the accuracy of the partial discharge recognition result of the power equipment. Moreover, the recognition result carries the confidence, which is helpful to reflect the determination degree of the target recognition model to the partial discharge recognition result, and is helpful to the on-site personnel or system to consider the reliability of the partial discharge recognition result when making decisions, thereby solving the technical problem of inaccurate partial discharge recognition of the power equipment.

[0051] The above method of this embodiment will be further introduced below.

[0052] As an optional embodiment, in step S102, at least one statistical parameter of the partial discharge pulse signal is determined based on the partial discharge pulse signal, including: pre-processing the partial discharge pulse signal to obtain a pre-processed partial discharge pulse signal, wherein the pre-processing is used to filter out high-frequency noise and low-frequency noise in the partial discharge pulse signal; and determining at least one statistical parameter of the pre-processed partial discharge pulse signal based on the pre-processed partial discharge pulse signal, wherein the statistical parameter at least includes one of the following: signal amplitude, signal frequency, and signal duration of the pre-processed partial discharge pulse signal.

[0053] In this embodiment, since in the actual environment, the partial discharge pulse signal is often accompanied by various noises, including high-frequency noise (such as electromagnetic interference, radio frequency signal) and low-frequency noise (such as power supply noise, vibration noise). By pre-processing the partial discharge pulse signal, these high-frequency noise and low-frequency noise can be filtered out, and a purer and more easily analyzed signal can be obtained. Optionally, common preprocessing techniques include filtering (such as low-pass filtering, high-pass filtering, band-pass filtering), signal denoising (such as wavelet denoising, median filtering), and signal enhancement (such as gain adjustment in time domain or frequency domain). Through pre-processing, the signal-to-noise ratio (SNR) of the partial discharge pulse signal, i.e., the ratio of signal strength to noise strength, can be improved, so that the subsequent feature extraction and analysis are more accurate.

[0054] Optionally, the statistical parameters of the pre-processed partial discharge pulse signal are the basis for subsequent identification of whether the power equipment has partial discharge. It should be noted that the statistical parameters include but are not limited to signal amplitude, signal frequency and signal duration, which are key features of partial discharge activity. Among them, the signal amplitude reflects the energy size of the partial discharge pulse signal; the signal frequency indicates the number of discharge events per unit time, which is related to the activity level of partial discharge; and the signal duration describes the time span of a single discharge event, which is crucial for understanding the physical process and mechanism of partial discharge. Determining these parameters is usually through calculating the mean, standard deviation, frequency component distribution, etc. of the partial discharge pulse signal, thereby providing a quantitative basis for subsequent pattern recognition and defect judgment.

[0055] In this step, the purity of the partial discharge pulse signal is ensured through preprocessing, which is a key step to avoid misidentification and improve recognition accuracy. The determination of statistical parameters is a quantitative description of signal characteristics from a mathematical and statistical point of view, which provides intuitive and quantitative features for subsequent significance analysis and model recognition. Through this process, it can ensure that the subsequent target recognition model can receive high-quality input signals, thereby more accurately identifying possible partial discharge phenomena and insulation defects in power equipment, which has important significance for the safe operation of power systems and equipment maintenance.

[0056] As an optional embodiment, in step S103, based on at least one statistical parameter, a statistical significance index of the partial discharge pulse signal is determined, including: performing standardization processing on the at least one statistical parameter to obtain a standardized value of the at least one statistical parameter; and performing weighted summation on the standardized value of the at least one statistical parameter to obtain the statistical significance index.

[0057] In this embodiment, the significance of the partial discharge pulse signal relative to the background noise is quantified through standardization processing and weighted summation, thereby providing a scientific basis for subsequent defect recognition and early warning.

[0058] Optionally, standardization processing is a data preprocessing technique used to eliminate the influence of the dimension of statistical parameters and convert them to a unified scale for comparison and analysis. In the identification of partial discharge pulse signals, standardization processing usually uses the Z-score method, where the Z-score calculation formula can be represented by the following formula:

[0059]

[0060] Wherein, Z is used to indicate the normalized value, X is used to indicate the actual value of the statistical parameter, μ is used to indicate the mean value of the statistical parameter, and σ is used to indicate the standard deviation of the statistical parameter. Through the normalization processing, the amplitude, frequency, duration and other parameters of the partial discharge pulse signal can be converted into dimensionless normalized values, and the size of the values reflects the deviation degree of the parameter value from the average value, that is, whether the parameter value deviates from the normal range significantly.

[0061] Optionally, after the statistical parameter is converted into the normalized value, the multiple normalized statistical parameter values can be fused by weighted summation to generate a comprehensive statistical significance index.

[0062] In this step, the influence of the dimension of the original parameter value is eliminated by the normalization processing of the statistical parameter, so that different statistical parameters can be compared and analyzed on the same scale, which is crucial for comprehensive evaluation of the significance degree of the partial discharge pulse signal. The weighted summation considers the contribution degree of different parameters in identifying the partial discharge, and through the comprehensive normalized values of multiple parameters, the abnormal degree of the partial discharge pulse signal can be more comprehensively reflected, and the accuracy and reliability of the identification are improved.

[0063] As an optional implementation, the weighted summation of the normalized values of the at least one statistical parameter obtains the statistical significance index, including: respectively determining the weight corresponding to the normalized value of the at least one statistical parameter, wherein the weight is used to represent the influence degree of the statistical parameter on whether the partial discharge pulse signal has statistical significance, and the weight is adjusted at least according to the discharge characteristics of the partial discharge pulse signal and the partial discharge pulse signal of the power equipment in the historical stage; and the weighted summation of the normalized values of the at least one statistical parameter and the weight corresponding to the normalized value obtains the statistical significance index.

[0064] In this embodiment, after obtaining the normalized value corresponding to each statistical parameter, the weight corresponding to each statistical parameter can be determined according to the relative importance of each statistical parameter in identifying the partial discharge of the power equipment, and then the weighted summation of the normalized value corresponding to each statistical parameter and the weight obtains the statistical significance index of the partial discharge pulse signal.

[0065] For example, the calculation formula of the weighted summation of the normalized values of the statistical parameters is:

[0066] Z = ω1Z A + ω2Z F + ω3Z D

[0067] Wherein, S is used to indicate the statistical significance index, ω1, ω2 and ω3 are the weights of the statistical parameters, and Z A , ZF and Z D are the Z-scores of amplitude, frequency, and duration, respectively, where the weights ω1, ω2, and ω3 can be adjusted according to the characteristics of the partial discharge pulses and historical data to optimize the accuracy of the statistical significance indicator.

[0068] Optionally, the determination of the weights can be based on historical data and expert knowledge, adjusted by analyzing the contribution of different statistical parameters in identifying different types of partial discharge, ensuring that the target recognition model focuses more on those statistical parameters that are more sensitive to discharge identification.

[0069] Optionally, after determining the statistical significance indicator, if the value of the statistical significance indicator is greater than (exceeds) the preset statistical significance threshold, it can be considered that the partial discharge pulse signal has statistical significance, that is, the discharge signal is not caused by random noise or interference, but truly exists in the power equipment and may be related to the insulation defect of the equipment. The higher or lower this indicator, the more serious the partial discharge phenomenon and the more urgent the need for further diagnosis.

[0070] Optionally, by calculating the weighted sum of the standardized values of the statistical parameters, the abnormality degree of the partial discharge pulse signal can be comprehensively evaluated from multiple angles, thereby improving the accuracy and reliability of defect identification. By introducing the concept of weight, the importance of each parameter can be dynamically adjusted according to historical data and device characteristics, making the model more adaptable to specific monitoring environments and power equipment types, enhancing the flexibility and intelligence of the identification method. The final statistical significance indicator provides a key quantitative basis for subsequent defect recognition model selection and state evaluation, which helps power system maintenance personnel to discover and handle potential insulation problems in time, ensuring the safe and stable operation of power equipment.

[0071] As an optional embodiment, in step S104, based on the comparison result of the statistical significance indicator and the statistical significance indicator threshold, a target recognition model is called to detect the partial discharge pulse signal to obtain a partial discharge recognition result of the power equipment, including: in response to the comparison result indicating that the statistical significance indicator is greater than the statistical significance indicator threshold, a phase-based statistical recognition model is called to detect the partial discharge pulse signal to obtain a first partial discharge recognition result of the power equipment; in response to the comparison result indicating that the statistical significance indicator is not greater than the statistical significance indicator threshold, a time series analysis model is called to detect the partial discharge pulse signal to obtain a second partial discharge recognition result of the power equipment.

[0072] In this embodiment, the statistical significance indicator of the partial discharge pulse signal is compared with the preset statistical significance indicator threshold, and then the corresponding defect recognition model is called, which is an important mechanism for realizing intelligent recognition of GIS insulation defects. This process adapts to the significance of the signal by dynamically selecting the model, thereby improving the accuracy and efficiency of recognition.

[0073] Optionally, after obtaining the statistical significance indicator, the calculated statistical significance indicator can be compared with the preset statistical significance indicator threshold to obtain a comparison result. If the statistical significance indicator is greater than the statistical significance indicator threshold, it indicates that the partial discharge pulse signal is significantly different from the background noise in a statistical sense and has a stable and identifiable pattern. At this time, a phase-based statistical recognition model (for example, a convolutional neural network (CNN) algorithm based on PRPD spectrum) can be called for further analysis. The phase-based statistical recognition model is good at extracting patterns from the phase and statistical characteristics of the partial discharge pulse signal and recognizing specific types of insulation defects. The calling of the phase-based statistical recognition model based on the significance of the signal can more accurately locate and recognize the defect type in the power equipment, and obtain a first partial discharge recognition result. The first partial discharge result can be regarded as a true value result and can be used to issue a fault warning instruction based on partial discharge monitoring.

[0074] Optionally, if the statistical significance indicator is not greater than the statistical significance indicator threshold, it may mean that the partial discharge activity in the partial discharge pulse signal exhibits weak statistical characteristics, or the partial discharge pulse signal is severely disturbed by noise and cannot immediately draw a clear conclusion through the phase-based statistical recognition model. At this time, a time series analysis model (for example, an algorithm based on LSTM) can be called to analyze the signal. The time series analysis model can capture the sequence characteristics of the partial discharge pulse signal over time, and even in the case of weak partial discharge pulse signal characteristics or large noise, it can recognize the discharge type by analyzing the time series pattern of the signal to obtain a second partial discharge recognition result.

[0075] In this step, the dynamic model selection based on signal significance enables the use of a more direct and efficient phase-based statistical recognition model for recognition when the signal characteristics are obvious, and the use of a time series analysis model for recognition when the signal characteristics are weak or there is a lot of noise. Such a mechanism not only improves the accuracy of recognition, but also enhances the adaptability and robustness of the system, which can effectively cope with complex and variable signal environments. At the same time, by giving the recognition results under different models (first and second partial discharge recognition results), the system can provide more comprehensive diagnostic information, which helps the operation and maintenance personnel to comprehensively judge the health status of the power equipment and take timely maintenance measures to ensure the safe and stable operation of the power system.

[0076] As an optional embodiment, after obtaining the second partial discharge identification result, the partial discharge identification method of the power equipment further includes: obtaining the partial discharge pulse signal of the power equipment within a second preset time length, wherein the second preset time length is longer than the first preset time length; determining the statistical significance index corresponding to the partial discharge pulse signal obtained within the second preset time length; in response to the statistical significance index corresponding to the partial discharge pulse signal obtained within the second preset time length being greater than the statistical significance index threshold, calling the phase-based statistical identification model to detect the partial discharge pulse signal of the power equipment obtained within the second preset time length, to obtain a third partial discharge identification result of the power equipment; and updating the second partial discharge identification result by using the third partial discharge identification result.

[0077] In this embodiment, when the statistical significance index does not reach the statistical significance index threshold, the partial discharge identification result obtained by calling the time sequence identification algorithm can be regarded as a probabilistic result. Since the partial discharge phenomenon may have hiddenness and intermittence, it may not be possible to fully capture the characteristics of such phenomenon by only using the signal within a short time window. Based on this, in order to improve the accuracy of the partial discharge identification of the power equipment, the identification result can be further verified and optimized by analyzing the discharge pulse signal within a longer time window, to ensure its accuracy and reliability.

[0078] For example, after obtaining the second partial discharge identification result, the partial discharge pulse signal within a second preset time length is obtained, wherein the second preset time length is usually longer than the first preset time length, then the foregoing steps of determining the partial discharge identification result are repeatedly executed, and the statistical significance index is further calculated, until the statistical significance index of the power equipment is greater than the statistical significance index threshold, indicating that the discharge phenomenon has a high enough statistical significance and can be considered as a real discharge event rather than background noise or accidental interference. At this time, the phase-based statistical identification model (such as a convolutional neural network model based on PRPD spectrum) is called to deeply analyze the signal within this time window, to identify the type and position of the possible partial discharge in the power equipment, to obtain a third partial discharge identification result. At this time, the third partial discharge identification result can also be regarded as a true value result, which can be used to issue a fault warning instruction based on the partial discharge monitoring.

[0079] Optionally, after obtaining the third partial discharge identification result, the second partial discharge identification result is updated by using the third partial discharge identification result. This process involves dynamic updating and optimization of the identification result, and by comparing and fusing the identification results within different time windows, the accuracy and stability of the identification can be improved.

[0080] Optionally, by extending the monitoring time window, increasing the amount of data and time dimension, further improve the accuracy and confidence of partial discharge identification. Especially in dealing with intermittent or changing discharge phenomenon, it can provide more comprehensive signal feature analysis, help to reduce misjudgment and missed judgment, ensure the health status monitoring and diagnosis of power equipment more accurate and reliable.

[0081] As an optional implementation, the discharge type is at least one of the following: corona discharge, surface discharge and suspension discharge.

[0082] Optionally, Figure 2 is a schematic diagram of corona discharge according to an embodiment of the present application, Figure 3 is a schematic diagram of surface discharge according to an embodiment of the present application, Figure 4 is a schematic diagram of suspension discharge according to an embodiment of the present application.

[0083] The technical solutions of the embodiments of the present application will be illustrated below in conjunction with preferred embodiments.

[0084] At present, power equipment, especially gas insulated metal enclosed switchgear (GIS) and other high-voltage electrical equipment, may have insulation defects during the design, manufacturing, transportation and installation process due to various uncontrollable factors. These defects manifest as partial discharge (PD) phenomena during the operation of power equipment, which are early signals of electrical equipment aging and potential failure. Partial discharge not only accelerates the deterioration of insulation materials, but also may cause more serious equipment failure, threatening the safety and stability of the power system. Therefore, accurately and timely identifying partial discharge and determining whether it is related to insulation defects is the key to preventing power equipment failure and ensuring the smooth operation of the power system.

[0085] In the related art, partial discharge identification mainly relies on two technical means: PRPD image analysis and PRPS image analysis. Among them, PRPD image can intuitively show the relationship between discharge pulse and voltage phase, while PRPS image emphasizes the periodicity and repeatability of discharge pulse. However, these identification methods are easily affected by the mixing of partial discharge signals and external interference signals, resulting in a decrease in identification accuracy and a display of application effect. In addition, due to the intermittent and statistical characteristics of partial discharge caused by many initial defects, traditional identification algorithms are difficult to make accurate judgments within a limited time or period. Moreover, the PRPD image analysis method and the PRPS image analysis method often require professionals to interpret PRPD and PRPS images based on experience and professional knowledge, which not only is inefficient, but also is easily affected by subjective judgment, and there is a technical problem of inaccurate partial discharge identification of power equipment.

[0086] However, the embodiment of the present application provides a GIS insulation defect identification method based on a partial discharge pulse statistical significance determination mechanism. First, the partial discharge pulses of the GIS are tracked and monitored, and the statistical significance of the monitored partial discharge pulses is determined, and according to the statistical significance processing logic, a phase base statistical identification model and a time sequence analysis model are respectively called, and the credibility of the model output result is simultaneously given. The statistical significance determination mechanism can statistically analyze the discharge pulse data, filter out the discharge events with significant significance, help to reduce the influence of noise and random errors on the identification result, and improve the accuracy of identification. By combining the phase base statistical identification model and the time sequence analysis model, the discharge pulse can be comprehensively analyzed from multiple angles, compared with the traditional single model identification, the discharge characteristics can be more comprehensively captured, and the accuracy of identification can be improved.

[0087] In the present application, after monitoring the partial discharge pulse signal of the power equipment in a time window, the statistical parameters of the partial discharge pulse in the window can be calculated, wherein the statistical parameters include but are not limited to: discharge amplitude, discharge frequency, discharge duration, wherein the discharge amplitude is the most intuitive parameter reflecting the severity of partial discharge. The average value of the pulse amplitude of the positive half cycle in the measurement time range of an integer multiple of a power frequency cycle is denoted as Aav+; the average value of the pulse amplitude of the positive half cycle is denoted as Aav-; the discharge frequency is the most intuitive parameter reflecting the activity of the partial discharge. The number of discharge pulses occurring in the positive half cycle in a unit measurement time t is denoted as N+; the number of discharge pulses occurring in the negative half cycle is denoted as N-. The discharge pulse duration is used to represent the dispersion degree of the time interval between the discharge pulses relative to the average value. When the variance is small, it indicates that the discharge pulses are evenly distributed; when the variance is large, it indicates that the discharge pulses are distributed in time.

[0088] Optionally, for the statistical parameter set composed of the discharge pulse amplitude, the discharge pulse frequency and the discharge pulse duration extracted above, the mean and the standard deviation of the statistical parameter set are calculated. Then the Z-score method is used to calculate the standardized value of each statistical parameter.

[0089] Optionally, the Z-scores of the statistical parameters are weighted and summed to obtain a statistical significance index. Wherein, the statistical significance indexes of the discharge pulses collected under three types of discharge are as follows:

[0090] Discharge type [CDATA[ω1]] <![CDATA[ω2]]> [["omega 3"]] ​ Z Corona discharge 0.4 0.5 0.1 5.24 Surface discharge 0.5 0.3 0.2 5.62 Suspension discharge 0.2 0.5 0.3 2.45

[0091] Wherein, ω1 is used to represent the weight corresponding to the discharge pulse amplitude, ω2 is used to represent the weight corresponding to the discharge pulse frequency, and ω3 is used to represent the weight corresponding to the discharge duration. Z is used to represent the statistical significance index calculated.

[0092] Optionally, assuming that the statistical significance index threshold is 3, it can be obtained from the above data that the corona discharge and the surface discharge data have statistical significance, and the floating discharge pulse data does not have statistical significance.

[0093] Optionally, for the discharge data set satisfying the statistical significance, a phase-based feature recognition model based on a CNN deep network can be used for partial discharge recognition, wherein, Figure 5 is a schematic diagram of a model framework of a phase-based feature recognition model according to an embodiment of the present application, as Figure 5 shown, the phase-based feature recognition model is composed of a convolutional layer, a pooling layer and a fully connected layer, and after the input data is processed through the convolutional layer, the pooling layer and the fully connected layer, the recognition result of the partial discharge can be obtained.

[0094] Optionally, the collected data is divided into a training set and a test set according to 8:2, wherein the training set is used to train the above model, wherein, Figure 6 is a schematic diagram of a confusion matrix of a phase-based feature recognition model according to an embodiment of the present application on a test set, as Figure 6 shown, by calculating the true positive TP (120), the false positive FP (7), the true negative TN (3) and the false negative TP (141) in the confusion matrix, the recognition accuracy of the model is obtained, wherein the calculation formula of the recognition accuracy of the model can be: (TP+TN) / (TP+TN+FP+FN), and the recognition accuracy of the model calculated by the formula is: 96.31%.

[0095] Optionally, for the real-time collected discharge data, it is determined to be surface discharge, and a fault early warning instruction of partial discharge monitoring is sent to the system. For the discharge data set not satisfying the statistical significance, a time sequence recognition model based on an LSTM unit is established, wherein, Figure 7 is a schematic diagram of a model framework of a time sequence recognition model according to an embodiment of the present application.

[0096] Optionally, the collected data is divided into a training set and a test set according to 8:2, wherein the training set is used to train the above model (time sequence recognition model), wherein, Figure 8 is a schematic diagram of a confusion matrix of a time sequence recognition model according to an embodiment of the present application on a test set, and based on the foregoing calculation method, the recognition accuracy of the model is: 91.14%.

[0097] Optionally, the statistical significance is converted into a confidence probability: (P 置信 for representing the confidence probability, S for representing the statistical significance index, S 阈值The probability of identifying the data in the current acquisition window as the suspended discharge is 97.62% according to the probability result of the recognition result of the statistical significance index threshold.

[0098] The beneficial effects of the present application are analyzed as follows.

[0099] In the present application, a statistical significance determination mechanism is introduced, which can perform statistical analysis on the discharge pulse data, filter out significant discharge events, help reduce the influence of noise and random errors on the recognition result, and improve the accuracy of recognition.

[0100] Further, the combination of the phase-based statistical recognition model and the time sequence analysis model can comprehensively analyze the discharge pulse from multiple angles. Compared with the traditional single model recognition, this comprehensive judgment method can more comprehensively capture the discharge characteristics and improve the accuracy of recognition.

[0101] Further, the model is dynamically selected according to the statistical significance result of the discharge pulse, so that the recognition process is more flexible and adaptive. This helps to adapt to the GIS insulation defect recognition needs under different discharge types and different working conditions. At the same time, the model has high scalability, and more analysis models and algorithms can be introduced according to actual needs to further improve the accuracy and adaptability of recognition.

[0102] Further, the automatic processing and analysis are realized by computer algorithms, which reduces the influence of manual intervention and subjective judgment. This helps to improve the efficiency and accuracy of recognition and reduce the operation and maintenance cost. By analyzing the characteristics and rules of the discharge pulse signal, the improvement and optimization design of the equipment can be guided, and the reliability and safety of the equipment can be improved.

[0103] According to the embodiments of the present application, a partial discharge recognition device of a power equipment is also provided. It should be noted that the partial discharge recognition device of the power equipment can be used to execute the partial discharge recognition method of the power equipment in the embodiments.

[0104] Figure 9 is a schematic diagram of a partial discharge recognition device of a power equipment according to an embodiment of the present application. As shown in Figure 9 the partial discharge recognition device 900 of the power equipment can include an acquisition unit 901, a first determination unit 902, a second determination unit 903, and a calling unit 904.

[0105] The acquisition unit 901 is configured to acquire a partial discharge pulse signal of the power equipment in a first preset time window, wherein the partial discharge pulse signal is used to represent the discharge characteristics of the power equipment in the first preset time window.

[0106] The first determination unit 902 is configured to determine at least one statistical parameter of the partial discharge pulse signal based on the partial discharge pulse signal, where the statistical parameter is used to represent a distribution characteristic of the partial discharge pulse signal.

[0107] The second determination unit 903 is configured to determine a statistical significance index of the partial discharge pulse signal based on the at least one statistical parameter, where the statistical significance index is used to quantify a significant degree of the partial discharge pulse relative to background noise, and the background noise is used to indicate an interference signal of the partial discharge pulse signal.

[0108] The calling unit 904 is configured to call a target recognition model to detect the partial discharge pulse signal based on a comparison result of the statistical significance index and a statistical significance index threshold value, to obtain a partial discharge recognition result of the power equipment, where the target recognition model includes a phase-based statistical recognition model and a time sequence analysis model, and the partial discharge recognition result at least includes a discharge type of the partial discharge of the power equipment and a confidence degree of the partial discharge recognition result, and the confidence degree is used to evaluate an accuracy degree of the partial discharge recognition result.

[0109] Optionally, the first determination unit 902 is further configured to: perform preprocessing on the partial discharge pulse signal to obtain a preprocessed partial discharge pulse signal, where the preprocessing is used to filter high-frequency noise and low-frequency noise in the partial discharge pulse signal; and determine at least one statistical parameter of the preprocessed partial discharge pulse signal based on the preprocessed partial discharge pulse signal, where the statistical parameter at least includes one of a signal amplitude, a signal frequency and a signal duration of the preprocessed partial discharge pulse signal.

[0110] Optionally, the second determination unit 903 is further configured to: perform preprocessing on the partial discharge pulse signal to obtain a preprocessed partial discharge pulse signal, where the preprocessing is used to filter high-frequency noise and low-frequency noise in the partial discharge pulse signal; and determine at least one statistical parameter of the preprocessed partial discharge pulse signal based on the preprocessed partial discharge pulse signal, where the statistical parameter at least includes one of a signal amplitude, a signal frequency and a signal duration of the preprocessed partial discharge pulse signal.

[0111] Optionally, the second determination unit 903 is further configured to: respectively determine a weight corresponding to a standardized value of the at least one statistical parameter, where the weight is used to represent an influence degree of the statistical parameter on whether the partial discharge pulse signal has statistical significance, and the weight is adjusted at least according to a discharge characteristic of the partial discharge pulse signal and a partial discharge pulse signal of the power equipment in a historical stage; and perform weighted summation on the standardized value of the at least one statistical parameter and the weight corresponding to the standardized value to obtain the statistical significance index.

[0112] Optionally, the calling unit 904 is further configured to: in response to the comparison result indicating that the statistical significance index is greater than the statistical significance index threshold, calling the phase-based statistical recognition model to detect the partial discharge pulse signal to obtain a first partial discharge recognition result of the power equipment; and in response to the comparison result indicating that the statistical significance index is not greater than the statistical significance index threshold, calling the time sequence analysis model to detect the partial discharge pulse signal to obtain a second partial discharge recognition result of the power equipment.

[0113] Optionally, the partial discharge recognition device 900 of the power equipment is further configured to: acquire the partial discharge pulse signal of the power equipment within a second preset time length, where the second preset time length is greater than the first preset time length; determine a statistical significance index corresponding to the partial discharge pulse signal acquired within the second preset time length; in response to the statistical significance index corresponding to the partial discharge pulse signal acquired within the second preset time length being greater than the statistical significance index threshold, calling the phase-based statistical recognition model to detect the partial discharge pulse signal of the power equipment acquired within the second preset time length to obtain a third partial discharge recognition result of the power equipment; and updating the second partial discharge recognition result by using the third partial discharge recognition result.

[0114] In this embodiment, the statistical significance determination mechanism is introduced, the statistical analysis can be performed on the partial discharge pulse signal of the power equipment, the statistical significance index of the partial discharge pulse signal of the power equipment is obtained, the discharge event with significant can be effectively screened out, and then the target recognition model is dynamically selected according to the comparison result of the statistical significance index and the statistical significance index threshold, so that the recognition process is more flexible and adaptive, which is helpful to adapt to the recognition requirements under different discharge types and different working conditions, and can improve the accuracy of the partial discharge recognition result of the power equipment. Moreover, the recognition result carries the confidence, which is helpful to reflect the determination degree of the target recognition model on the partial discharge recognition result, and is helpful to the on-site personnel or system to consider the reliability of the partial discharge recognition result when making a decision, thereby solving the technical problem of inaccurate partial discharge recognition of the power equipment.

[0115] According to the embodiment of the present application, a computer readable storage medium is also provided, which includes a stored program, wherein the program executes the partial discharge recognition method of the power equipment in the embodiment.

[0116] According to the embodiment of the present application, a processor is also provided, which is used to run a program, wherein the program runs to execute the partial discharge recognition method of the power equipment in the embodiment.

[0117] According to another aspect of the embodiment of the present application, a computer program product is also provided. The program product includes computer instructions, which are executed by a processor to implement the partial discharge recognition method of the power equipment in the embodiment.

[0118] The above-mentioned embodiment numbers of the present application are only for description, and do not represent the advantages and disadvantages of the embodiments.

[0119] In the above-mentioned embodiments of the present application, the description of each embodiment has its own focus, and the parts not described in detail in a certain embodiment can be referred to the relevant description of other embodiments.

[0120] In several embodiments provided in the present application, it should be understood that the disclosed technical contents can be implemented by other ways. Among them, the above-mentioned device embodiments are only schematic, for example, the division of units can be a logical function division, and actual implementation can have another division way, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units or modules shown or discussed can be indirect coupling or communication connection through some interfaces, and can be electrical or other forms.

[0121] The units described as separate components can or can not be physically separate, and the components shown as units can or can not be physical units, that is, they can be located in one place, or can be distributed to multiple units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiment scheme.

[0122] In addition, each functional unit in each embodiment of the present application can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be realized in the form of hardware or in the form of software functional unit.

[0123] If the integrated unit is realized in the form of software functional unit and sold or used as an independent functional component, it can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application or the whole or part of the technical solutions which essentially contribute to the prior art can be embodied in the form of software functional components, and the computer software functional components are stored in a storage medium, including a plurality of instructions for making a computer device (which can be a personal computer, a server or a network device, etc.) execute all or part of the steps of the embodiments of the present application. The above-mentioned storage medium includes: U disk, read-only memory (ROM), random access memory (RAM), mobile hard disk, magnetic disk or optical disk and various program code storage media.

[0124] The above merely is the preferred embodiment of the present application, it should be pointed out that, for ordinary skilled in the art, without departing from the principles of the present application, can also make a number of improvements and refinements, these improvements and refinements should also be considered as the protection scope of the present application.

Claims

1. A method for identifying partial discharge of power equipment, characterized in that: include: Acquiring a partial discharge pulse signal of the power equipment within a first preset time window, wherein the partial discharge pulse signal is used to characterize a discharge characteristic of the power equipment within the first preset time window; Determining at least one statistical parameter of the partial discharge pulse signal based on the partial discharge pulse signal, wherein the statistical parameter is used to characterize a distribution characteristic of the partial discharge pulse signal, and the statistical parameter includes at least one of the following: a signal amplitude, a signal frequency, and a signal duration of the preprocessed partial discharge pulse signal; determining a statistical significance index of the partial discharge pulse signal based on at least one of the statistical parameters, wherein the statistical significance index is used to quantify the significance of the partial discharge pulse relative to background noise, and the background noise is used to indicate an interference signal of the partial discharge pulse signal; Based on a comparison result between the statistical significance index and a statistical significance index threshold, calling a target recognition model to detect the partial discharge pulse signal to obtain a partial discharge recognition result of the power equipment, wherein the target recognition model includes a phase-based statistical recognition model and a time series analysis model, and the partial discharge recognition result includes at least a discharge type of partial discharge of the power equipment and a confidence level of the partial discharge recognition result, wherein the confidence level is used to evaluate the accuracy of the partial discharge recognition result; Wherein, determining the statistical significance index of the partial discharge pulse signal based on at least one of the statistical parameters includes: performing standardization processing on at least one of the statistical parameters to obtain a standardized value of at least one of the statistical parameters; and performing weighted summation on the standardized value of at least one of the statistical parameters to obtain the statistical significance index.

2. The method according to claim 1, characterized in that Determining at least one statistical parameter of the partial discharge pulse signal based on the partial discharge pulse signal includes: Preprocessing the partial discharge pulse signal to obtain a preprocessed partial discharge pulse signal, wherein the preprocessing is used to indicate filtering out high-frequency noise and low-frequency noise in the partial discharge pulse signal; At least one statistical parameter of the pre-processed partial discharge pulse signal is determined based on the pre-processed partial discharge pulse signal.

3. The method according to claim 1, characterized in that Performing a weighted summation on the standardized values ​​of at least one of the statistical parameters to obtain the statistical significance index comprises: Determining a weight corresponding to a standardized value of at least one of the statistical parameters, respectively, wherein the weight is used to indicate whether the statistical parameter has a statistically significant influence on the partial discharge pulse signal, and the weight is adjusted based on at least the discharge characteristics of the partial discharge pulse signal and the partial discharge pulse signals of the power equipment in a historical stage; The statistical significance index is obtained by performing weighted summation on the standardized value of at least one of the statistical parameters and the weight corresponding to the standardized value.

4. The method according to claim 1, wherein Based on the comparison result of the statistical significance index and the statistical significance index threshold, calling the target recognition model to detect the partial discharge pulse signal to obtain the partial discharge recognition result of the power equipment, including: In response to the comparison result indicating that the statistical significance index is greater than the statistical significance index threshold, calling the phase-based statistical identification model to detect the partial discharge pulse signal to obtain a first partial discharge identification result of the power equipment; In response to the comparison result indicating that the statistical significance index is not greater than the statistical significance index threshold, the timing analysis model is called to detect the partial discharge pulse signal to obtain a second partial discharge identification result of the power equipment.

5. The method according to claim 4, characterized in that After obtaining the second partial discharge identification result, the method further includes: Acquiring a partial discharge pulse signal of the electrical equipment within a second preset time period, wherein the second preset time period is greater than the first preset time period; Determining a statistical significance index corresponding to the partial discharge pulse signal acquired within the second preset time period; In response to a statistical significance index corresponding to the partial discharge pulse signal acquired within the second preset time period being greater than the statistical significance index threshold, calling the phase-based statistical identification model to detect the partial discharge pulse signal of the power equipment acquired within the second preset time period to obtain a third partial discharge identification result of the power equipment; The second partial discharge identification result is updated using the third partial discharge identification result.

6. The method according to claim 1, characterized in that The discharge type is at least one of the following: corona discharge, creeping discharge and suspension discharge.

7. A partial discharge identification device for power equipment, characterized in that: include: an acquisition unit, configured to acquire a partial discharge pulse signal of the power equipment within a first preset time window, wherein the partial discharge pulse signal is used to characterize a discharge characteristic of the power equipment within the first preset time window; a first determining unit, configured to determine at least one statistical parameter of the partial discharge pulse signal based on the partial discharge pulse signal, wherein the statistical parameter is used to characterize a distribution feature of the partial discharge pulse signal, and the statistical parameter includes at least one of the following: a signal amplitude, a signal frequency, and a signal duration of the preprocessed partial discharge pulse signal; a second determining unit, configured to determine a statistical significance index of the partial discharge pulse signal based on at least one of the statistical parameters, wherein the statistical significance index is used to quantify a significance of the partial discharge pulse relative to background noise, and the background noise is used to indicate an interference signal of the partial discharge pulse signal; a calling unit, configured to call a target recognition model to detect the partial discharge pulse signal based on a comparison result between the statistical significance index and a statistical significance index threshold, so as to obtain a partial discharge recognition result of the power equipment, wherein the target recognition model includes a phase-based statistical recognition model and a time series analysis model, and the partial discharge recognition result includes at least a discharge type of partial discharge of the power equipment and a confidence level of the partial discharge recognition result, wherein the confidence level is used to evaluate the accuracy of the partial discharge recognition result; The second determination unit is configured to determine the statistical significance index of the partial discharge pulse signal based on at least one of the statistical parameters through the following steps: performing standardization processing on at least one of the statistical parameters to obtain a standardized value of at least one of the statistical parameters; and performing weighted summation on the standardized value of at least one of the statistical parameters to obtain the statistical significance index.

8. A computer-readable storage medium, characterized in that The computer-readable storage medium includes a stored program, wherein when the program is executed by a processor, the device where the storage medium is located is controlled to execute the method according to any one of claims 1 to 6.

9. An electronic device, characterized in that: include: a memory storing an executable program; A processor, configured to run the program, wherein the program executes the method according to any one of claims 1 to 6 when running.

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