A method and system for comprehensive monitoring and analysis of partial discharge data of power equipment

Abstract

The application discloses a kind of power equipment partial discharge data comprehensive monitoring and analysis method and system, it is related to power equipment partial discharge monitoring and analysis technical field, wherein, power equipment partial discharge data comprehensive monitoring and analysis method, comprising: S1: according to the preset monitoring frequency acquisition comprehensive monitoring data;S2: the original signal in comprehensive monitoring data is classified and handled, determines current discharge category;S3: according to current discharge category, equipment data and environmental data carry out discharge condition analysis, obtain comprehensive discharge analysis result;When comprehensive discharge analysis result is critical, send comprehensive discharge analysis result, and obtain adjusted monitoring frequency, and the adjusted monitoring frequency is as the preset monitoring frequency, executes S1;When comprehensive discharge analysis result is abnormal, obtain maintenance data, and send comprehensive discharge analysis result and maintenance data.The application can improve the comprehensiveness, accuracy and timeliness of analysis.

Description

Technical Field

[0001] The present application relates to the technical field of partial discharge monitoring and analysis of power equipment, and in particular to a comprehensive monitoring and analysis method and system for partial discharge data of power equipment. Background Art

[0002] Partial discharge (PD) in power equipment refers to discharge that occurs only in a localized area of ​​the insulation within the equipment, without penetrating the entire device. PD can produce corresponding electrical, optical, thermal, and chemical phenomena, corroding the insulating medium and causing further insulation degradation, ultimately leading to equipment failure. Therefore, monitoring and analyzing PD data in power equipment is crucial to ensuring the safe operation of power equipment. However, existing methods and systems for monitoring and analyzing PD data in power equipment have the following problems:

[0003] (1) In the same period, power equipment may have one type of discharge situation or multiple different types of discharge situations at the same time. However, the existing monitoring and analysis methods and systems for partial discharge data of power equipment can usually only analyze a single type of discharge data. Therefore, the analysis results obtained are relatively one-sided and have low accuracy.

[0004] (2) Existing monitoring and analysis methods and systems for partial discharge data of power equipment usually do not fully consider the impact of different discharge categories, equipment data and environmental data on the analysis results of partial discharge conditions. Their analysis is incomplete and has low accuracy.

[0005] (3) The existing monitoring and analysis methods and systems for partial discharge data of power equipment cannot adjust the monitoring frequency in time according to the real-time situation, and cannot guarantee the real-time nature of monitoring.

[0006] Therefore, there is an urgent need to develop a comprehensive monitoring and analysis method and system for partial discharge data of power equipment to solve the above problems. Summary of the Invention

[0007] The purpose of this application is to provide a comprehensive monitoring and analysis method and system for partial discharge data of power equipment, which can improve the comprehensiveness, accuracy and timeliness of the analysis.

[0008] To achieve the above-mentioned objectives, the present application provides a comprehensive monitoring and analysis method for partial discharge data of power equipment, comprising the following steps: S1: obtaining comprehensive monitoring data according to a preset monitoring frequency, wherein the comprehensive monitoring data includes at least: original signals, equipment data and environmental data; S2: classifying and processing the original signals in the comprehensive monitoring data to determine the current discharge category, wherein the current discharge category includes at least one or more of: internal discharge, corona discharge, surface discharge, floating potential discharge and poor contact discharge; S3: analyzing the discharge situation according to the current discharge category, equipment data and environmental data to obtain a comprehensive discharge analysis result, wherein the comprehensive discharge analysis result is: normal, abnormal or critical; S4: when the comprehensive discharge analysis result is normal, sending the comprehensive discharge analysis result; when the comprehensive discharge analysis result is critical, sending the comprehensive discharge analysis result, and obtaining an adjusted monitoring frequency, and using the adjusted monitoring frequency as the preset monitoring frequency, executing S1; when the comprehensive discharge analysis result is abnormal, obtaining maintenance data, and sending the comprehensive discharge analysis result and maintenance data, wherein the maintenance data includes at least: the discharge location, the current discharge category and the risk index corresponding to each discharge category in the current discharge category.

[0009] As above, the sub-steps of classifying and processing the original signal in the comprehensive monitoring data to determine the current discharge category are as follows: S21: classifying and processing the ultra-high frequency electromagnetic wave signal in the original signal to determine the first discharge category; S22: classifying and processing the ultrasonic signal in the original signal to determine the second discharge category; S23: classifying and processing the high-frequency current pulse signal in the original signal to determine the third discharge category; S24: taking the first discharge category, the second discharge category and the third discharge category as the current discharge category.

[0010] As above, the sub-steps of classifying and processing the ultra-high frequency electromagnetic wave signal in the original signal and determining the first discharge category are as follows: S211: performing a first preprocessing on the ultra-high frequency electromagnetic wave signal in the original signal to obtain a preprocessed ultra-high frequency electromagnetic wave signal, wherein the first preprocessing at least includes: noise reduction processing and filtering processing; S212: performing feature extraction on the preprocessed ultra-high frequency electromagnetic wave signal to obtain a first local discharge feature, wherein the first local discharge feature includes: a first time domain feature, a first frequency domain feature, a first time-frequency domain feature and a statistical feature; S213: inputting the first local discharge feature into a pre-trained first classification model, and the first classification model analyzes the first local discharge feature to determine the first discharge category, wherein the first discharge category is: internal discharge, corona discharge or surface discharge.

[0011] As above, the ultrasonic signal in the original signal is classified and processed, and the sub-steps of determining the second discharge category are as follows: S221: performing a second preprocessing on the ultrasonic signal in the original signal to obtain a preprocessed ultrasonic signal, wherein the second preprocessing at least includes: denoising and normalization; S222: performing feature extraction on the preprocessed ultrasonic signal to obtain a second local discharge feature, wherein the second local discharge feature includes: a second time domain feature, a second frequency domain feature and a second time-frequency domain feature; S223: inputting the second local discharge feature into a pre-trained second classification model, and the second classification model analyzes the second local discharge feature to determine the second discharge category, wherein the second discharge category is: internal discharge, surface discharge or corona discharge.

[0012] As above, the high-frequency current pulse signal in the original signal is classified and processed, and the sub-steps of determining the third discharge category are as follows: S231: performing a third preprocessing on the high-frequency current pulse signal in the original signal to obtain a preprocessed high-frequency current pulse signal, wherein the third preprocessing at least includes: filtering processing and normalization processing; S232: performing feature extraction on the preprocessed high-frequency current pulse signal to obtain a third local discharge feature, wherein the third local discharge feature includes: a third time domain feature, a third frequency domain feature, a third time-frequency domain feature and a second statistical feature; S233: inputting the third local discharge feature into a pre-trained third classification model, and the third classification model analyzes the third local discharge feature to determine a third discharge category, wherein the third discharge category is: internal discharge, floating potential discharge or poor contact discharge.

[0013] As above, the discharge situation is analyzed according to the current discharge category, equipment data and environmental data, and the sub-steps for obtaining the comprehensive discharge analysis results are as follows: S31: read the current discharge category, and randomly generate an analysis serial number for each discharge category in the current discharge category, and the analysis serial number increases in random order; S32: the discharge category corresponding to the minimum value in the analysis serial number is used as the target discharge category, and the impact weight database is traversed according to the target discharge category to determine the impact weight data packet that is consistent with the standard discharge category and the target discharge category as the target weight data packet, wherein the target weight data packet includes at least: discharge category hazard value, hazard degree weight, equipment factor weight, each The weight of each device indicator, the weight of the environmental factor and the weight of each environmental indicator; S33: Calculate the target discharge evaluation value according to the target weight data packet, device data and environmental data; S34: Judge the analysis sequence number of the target discharge category according to the total number of discharge categories in the current discharge category. If the total number of discharge categories in the current discharge category is greater than the analysis sequence number of the target discharge category, eliminate the analysis sequence number of the target discharge category and execute S32; if the total number of discharge categories in the current discharge category is equal to the analysis sequence number of the target discharge category, eliminate the analysis sequence number of the target discharge category and execute S35; S35: Analyze all target discharge evaluation values ​​to obtain a comprehensive discharge analysis result.

[0014] As above, the expression of the target discharge evaluation value is:

[0015] Among them, Spg i The target discharge evaluation value corresponding to the target discharge category with analysis number i; To analyze the hazard degree weight in the target weight data packet corresponding to the target discharge category with sequence number i; Fys i To analyze the discharge category hazard value in the target weight data packet corresponding to the target discharge category with sequence number i; To analyze the equipment factor weight in the target weight data packet corresponding to the target discharge category with sequence number i; Szb n is the normalized value of the nth device indicator in the device data; η n To analyze the weight of the nth device indicator in the target weight data packet corresponding to the target discharge category with sequence number i, n∈[1,N], N is a natural number representing the total number of device indicators in the device data; To analyze the environmental factor weight in the target weight data packet corresponding to the target discharge category with sequence number i; Hzb m is the normalized value of the mth environmental indicator in the environmental data; mTo analyze the weight of the mth environmental indicator in the target weight data packet corresponding to the target discharge category with serial number i, m∈[1,M], M is a natural number, representing the total number of environmental indicators in the environmental data.

[0016] As above, all target discharge evaluation values ​​are analyzed to obtain the sub-steps of the comprehensive discharge analysis result as follows: S351: traverse the evaluation threshold database according to the target discharge category corresponding to each target discharge evaluation value, and determine the standard threshold table whose standard discharge category is consistent with the target discharge category as the target threshold table; S352: use the target threshold table to analyze the corresponding target discharge evaluation value to generate a sub-discharge result. If the target discharge evaluation value is within the normal threshold range of the target threshold table, the generated sub-discharge result is normal; if the target discharge evaluation value is within the critical threshold range of the target threshold table, the generated sub-discharge result is critical; if the target discharge evaluation value is within the abnormal threshold range of the target threshold table, the generated sub-discharge result is abnormal; S353: generate a comprehensive discharge analysis result based on all sub-discharge results; if all sub-discharge results are normal, the generated comprehensive discharge analysis result is normal; if there are one or more abnormalities in all sub-discharge results, the generated comprehensive discharge analysis result is abnormal; if there are one or more criticalities in all sub-discharge results and no abnormalities, the comprehensive discharge analysis result is critical.

[0017] As above, the sub-steps for obtaining the adjusted monitoring frequency are as follows: S41: Obtain historical monitoring data, and obtain a frequency index value based on the historical monitoring data and the target discharge evaluation value, wherein the historical monitoring data includes: the total number of current monitoring times and the total number of current abnormalities; S42: Traverse the monitoring frequency data table according to the frequency index value, and determine that the adjustment frequency value corresponding to the frequency index range to which the frequency index value belongs is the target adjustment frequency value; S43: Obtain the adjusted monitoring frequency based on the target adjustment frequency value and the preset monitoring frequency, and the adjusted monitoring frequency = preset monitoring frequency + target adjustment frequency value.

[0018] The present application also provides a comprehensive monitoring and analysis system for partial discharge data of power equipment, including: multiple user terminals and a comprehensive monitoring and analysis center; wherein the user terminals are used to send comprehensive monitoring data to the comprehensive monitoring and analysis center according to a preset monitoring frequency; receive comprehensive discharge analysis results and maintenance data; and the comprehensive monitoring and analysis center is used to execute the above-mentioned comprehensive monitoring and analysis method for partial discharge data of power equipment.

[0019] The beneficial effects achieved by this application are as follows:

[0020] (1) The comprehensive monitoring and analysis method and system for partial discharge data of power equipment of the present application can monitor and analyze a variety of discharge data, and the analysis results are comprehensive and accurate.

[0021] (2) The comprehensive monitoring and analysis method and system for partial discharge data of power equipment of the present application fully considers the impact of different discharge categories, equipment data and environmental data on the analysis results of partial discharge conditions, effectively improving the comprehensiveness and accuracy of the analysis.

[0022] (3) The comprehensive monitoring and analysis method and system of partial discharge data of power equipment of the present application can timely adjust the monitoring frequency according to the real-time situation, thereby ensuring the real-time and accuracy of the monitoring.

[0023] (4) The comprehensive monitoring and analysis method and system of partial discharge data of power equipment of the present application can obtain predicted discharge data when the comprehensive discharge analysis result is critical. The predicted discharge data can assist the user terminal to know in advance the time and location of possible equipment failure, so as to facilitate the arrangement of maintenance plans in advance, thereby avoiding serious consequences such as production interruption and safety accidents caused by sudden equipment failure; and can timely perform maintenance and repair on the equipment before it is seriously damaged, thereby reducing the wear and aging of the equipment caused by excessive discharge or abnormal discharge, and extending the service life of the equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments recorded in this application. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0025] Figure 1 A schematic structural diagram of an embodiment of a comprehensive monitoring and analysis system for partial discharge data of power equipment;

[0026] Figure 2 The present invention is a flow chart of an embodiment of a method for comprehensive monitoring and analysis of partial discharge data of power equipment. DETAILED DESCRIPTION

[0027] The following is a clear and complete description of the technical solutions in the embodiments of the present invention, in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0028] like Figure 1 As shown, the present application provides a comprehensive monitoring and analysis system for partial discharge data of power equipment, including: multiple user terminals 1 and a comprehensive monitoring and analysis center 2.

[0029] Among them, the user terminal 1 is used to send comprehensive monitoring data to the comprehensive monitoring and analysis center 2 according to the preset monitoring frequency; and receive comprehensive discharge analysis results and maintenance data.

[0030] Integrated Monitoring and Analysis Center 2: used to implement the following integrated monitoring and analysis method for partial discharge data of power equipment.

[0031] Furthermore, the integrated monitoring and analysis center 2 at least includes: an acquisition unit, a classification unit, a first analysis unit, a second analysis unit, a third analysis unit and a storage unit.

[0032] Among them, the acquisition unit: acquires comprehensive monitoring data according to a preset monitoring frequency, wherein the comprehensive monitoring data at least includes: original signals, equipment data and environmental data; receives and sends comprehensive discharge analysis results, adjusted monitoring frequency and maintenance data.

[0033] Classification unit: classifies and processes the original signals in the comprehensive monitoring data to determine the current discharge category, wherein the current discharge category includes at least one or more of: internal discharge, corona discharge, surface discharge, floating potential discharge and poor contact discharge.

[0034] The first analysis unit analyzes the discharge situation according to the current discharge category, device data, and environmental data to obtain a comprehensive discharge analysis result, wherein the comprehensive discharge analysis result is: normal, abnormal, or critical.

[0035] The second analysis unit: when the comprehensive discharge analysis result is critical, obtains the adjusted monitoring frequency and uses the adjusted monitoring frequency as the preset monitoring frequency.

[0036] The third analysis unit: when the comprehensive discharge analysis result is abnormal, obtains maintenance data.

[0037] Storage unit: used to store the impact weight database, assessment threshold database, monitoring frequency data table and risk level database.

[0038] Among them, the impact weight database includes: multiple impact weight data packets, an impact weight table corresponds to a standard discharge category, and each impact weight data packet includes at least: discharge category hazard value, hazard degree weight, equipment factor weight, weight of each equipment indicator, environmental factor weight and weight of each environmental indicator.

[0039] Standard discharge categories include at least internal discharge, corona discharge, creeping discharge, floating potential discharge, and poor contact discharge. These categories directly reflect the degree of damage caused by the discharge.

[0040] Specifically, the discharge category hazard value is a pre-assigned score used to characterize the degree of hazard that the discharge category poses to the device. Different discharge categories have different degrees of hazard to the device, so the discharge category hazard values ​​corresponding to different standard discharge categories vary.

[0041] The evaluation threshold database includes multiple standard threshold tables, one standard threshold table corresponds to one standard discharge category, and each standard threshold table includes: a normal threshold range, a critical threshold range, and an abnormal threshold range.

[0042] Specifically, the expression of the normal threshold range is: [Zyz min ,Zyz max ], where Zyz min is the minimum threshold value when the discharge condition is normal; Zyz max It is the maximum threshold value when the discharge condition is normal.

[0043] The expression of critical threshold range is: [Lyz min ,Lyz max ], among which Lyz min The minimum threshold value when the discharge is normal but about to become abnormal; Lyz max This is the maximum threshold when the discharge condition is normal but about to become abnormal.

[0044] The expression of abnormal threshold range is: [Gyz min ,Gyz max ], where Gyz min Gyz is the minimum threshold when the discharge condition is abnormal; max It is the maximum threshold when the discharge condition is abnormal.

[0045] The monitoring frequency data table includes multiple frequency index ranges, and one frequency index range corresponds to one adjustment frequency value.

[0046] The risk level database includes multiple risk level data packets, one risk level data packet corresponds to one standard discharge category, each risk level data packet includes multiple risk level ranges, and one risk level range corresponds to one risk level.

[0047] Furthermore, based on artificial intelligence technology or deep learning technology, multiple actual monitoring and analysis data and / or multiple experimental data are analyzed and processed, so as to update the impact weight database, assessment threshold database, monitoring frequency data table and risk level database in real time to ensure the comprehensiveness, timeliness and accuracy of the impact weight database, assessment threshold database, monitoring frequency data table and risk level database.

[0048] Furthermore, the comprehensive monitoring and analysis center 2 further includes: a prediction unit; wherein the prediction unit: when the comprehensive discharge analysis result is critical, obtains predicted discharge data and sends the predicted discharge data to the user terminal.

[0049] like Figure 2 As shown, the present application provides a comprehensive monitoring and analysis method for partial discharge data of power equipment, comprising the following steps:

[0050] S1: Obtain comprehensive monitoring data according to a preset monitoring frequency, wherein the comprehensive monitoring data at least includes: original signals, equipment data and environmental data.

[0051] The original signal includes at least: an ultra-high frequency electromagnetic wave signal, an ultrasonic wave signal and a high frequency current pulse signal.

[0052] Device data can directly reflect the device status. Device data includes at least multiple device indicators, such as device voltage, device current, device temperature, and insulation performance.

[0053] Environmental data refers to external factors that affect discharge, and includes at least a plurality of environmental indicators, such as temperature data, humidity data, pollution level, and altitude.

[0054] S2: Classify and process the original signals in the comprehensive monitoring data to determine the current discharge category, wherein the current discharge category includes at least one or more of internal discharge, corona discharge, surface discharge, floating potential discharge, and poor contact discharge.

[0055] Furthermore, the sub-steps of classifying and processing the original signal in the comprehensive monitoring data and determining the current discharge category are as follows:

[0056] S21: Classify and process the ultra-high frequency electromagnetic wave signal in the original signal to determine a first discharge category.

[0057] Furthermore, the sub-steps of classifying and processing the ultra-high frequency electromagnetic wave signal in the original signal to determine the first discharge category are as follows:

[0058] S211: Performing a first preprocessing on the ultra-high frequency electromagnetic wave signal in the original signal to obtain a preprocessed ultra-high frequency electromagnetic wave signal, wherein the first preprocessing at least includes: noise reduction processing and filtering processing.

[0059] Specifically, the UHF electromagnetic wave signal in the original signal is subjected to noise reduction processing to obtain the UHF electromagnetic wave signal after noise reduction, and the UHF electromagnetic wave signal after noise reduction is subjected to filtering processing, which can improve the quality of the UHF electromagnetic wave signal.

[0060] The noise reduction process can be implemented by using existing technologies, such as wavelet noise reduction, empirical mode decomposition (EMD) noise reduction, or noise reduction based on statistical models.

[0061] The filtering process can be implemented using existing technologies, such as low-pass filtering, band-pass filtering or adaptive filtering.

[0062] S212: Extract features from the preprocessed ultra-high frequency electromagnetic wave signal to obtain a first partial discharge feature, wherein the first partial discharge feature includes: a first time domain feature, a first frequency domain feature, a first time-frequency domain feature, and a statistical feature.

[0063] Specifically, the first time domain feature is to calculate the peak value, effective value, kurtosis and skewness of the preprocessed ultra-high frequency electromagnetic wave signal. The peak value can reflect the intensity of the discharge, the effective value can reflect the overall energy level, and the kurtosis and skewness can characterize the distribution characteristics of the preprocessed ultra-high frequency electromagnetic wave signal.

[0064] First, frequency domain characteristics: The pre-processed UHF electromagnetic wave signal is converted to the frequency domain through fast Fourier transform, and the center frequency, bandwidth and amplitude of each frequency component are extracted. Different discharge categories have different characteristic distributions in the frequency domain.

[0065] First, time-frequency domain characteristics: Use time-frequency analysis methods (such as wavelet transform, short-time Fourier transform, etc.) to obtain the energy distribution characteristics of the pre-processed ultra-high frequency electromagnetic wave signal on the time-frequency plane, such as the energy concentration area and change trend in the time-frequency diagram.

[0066] Statistical characteristics: Calculate the statistics of the pre-processed UHF electromagnetic wave signal, such as probability density function and power spectrum density, and analyze its distribution pattern and statistical characteristics to distinguish different discharge categories.

[0067] S213: Inputting the first partial discharge feature into a pre-trained first classification model, and having the first classification model analyze the first partial discharge feature to determine a first discharge category, wherein the first discharge category is: internal discharge, corona discharge, or creeping discharge.

[0068] Specifically, the first classification model is a pre-trained deep learning model (for example, a CNN (convolutional neural network)-LSTM (long short-term memory network) hybrid neural network), a support vector machine, artificial intelligence, or a decision tree.

[0069] S22: Classify the ultrasonic signal in the original signal to determine a second discharge category.

[0070] Furthermore, the sub-steps of classifying and processing the ultrasonic signal in the original signal to determine the second discharge category are as follows:

[0071] S221: Performing a second preprocessing on the ultrasonic signal in the original signal to obtain a preprocessed ultrasonic signal, wherein the second preprocessing at least includes: denoising processing and normalization processing.

[0072] Specifically, the ultrasonic signal in the original signal is denoised to obtain a denoised ultrasonic signal, which can remove random noise and smooth the signal. The denoised ultrasonic signal is normalized to a preset range to eliminate the impact of different amplitudes on subsequent processing.

[0073] The denoising process of the ultrasonic signal in the original signal can be achieved by using existing denoising processing methods, such as mean filtering and median filtering.

[0074] The normalization processing of the denoised ultrasonic signal can be achieved by using existing technology.

[0075] S222: Perform feature extraction on the preprocessed ultrasonic signal to obtain a second partial discharge feature, wherein the second partial discharge feature includes: a second time domain feature, a second frequency domain feature, and a second time-frequency domain feature.

[0076] Specifically, the second time domain feature is to calculate the peak value, effective value, kurtosis and skewness of the preprocessed ultrasonic signal. The peak value can reflect the intensity of the discharge, the effective value can reflect the overall energy level, and the kurtosis and skewness can characterize the distribution characteristics of the preprocessed ultrasonic signal.

[0077] Second frequency domain features: The preprocessed ultrasonic signal is converted to the frequency domain through fast Fourier transform, and the center frequency, bandwidth and amplitude of each frequency component are extracted. Different discharge categories have different characteristic distributions in the frequency domain.

[0078] Second, time-frequency domain features: Use time-frequency analysis methods (such as wavelet transform, short-time Fourier transform, etc.) to obtain the energy distribution characteristics of the preprocessed ultrasonic signal on the time-frequency plane, such as the energy concentration area and change trend in the time-frequency diagram.

[0079] S223: Inputting the second partial discharge feature into a pre-trained second classification model, and having the second classification model analyze the second partial discharge feature to determine a second discharge category, wherein the second discharge category is: internal discharge, surface discharge, or corona discharge.

[0080] Specifically, the second classification model is a model with a feature selection mechanism such as logistic regression, artificial intelligence or decision tree.

[0081] S23: Classify the high-frequency current pulse signal in the original signal to determine a third discharge category.

[0082] Furthermore, the high-frequency current pulse signal in the original signal is classified and processed to determine the third discharge category in the following sub-steps:

[0083] S231: Perform a third preprocessing on the high-frequency current pulse signal in the original signal to obtain a preprocessed high-frequency current pulse signal, wherein the third preprocessing at least includes: filtering processing and normalization processing.

[0084] Specifically, the high-frequency current pulse signal in the original signal is filtered to obtain a filtered high-frequency current pulse signal, and the high-frequency current pulse signal in the filtered signal is normalized to obtain a preprocessed high-frequency current pulse signal.

[0085] The high-frequency current pulse signal in the original signal can be filtered using existing technologies, such as digital filtering technology.

[0086] The normalization processing of the high-frequency current pulse signal in filtering can be achieved by using the existing technology.

[0087] S232: Extract features from the preprocessed high-frequency current pulse signal to obtain a third partial discharge feature, wherein the third partial discharge feature includes: a third time domain feature, a third frequency domain feature, a third time-frequency domain feature, and a second statistical feature.

[0088] Specifically, the third time domain feature is to calculate the peak value, effective value, kurtosis and skewness of the preprocessed high-frequency current pulse signal. The peak value can reflect the intensity of the discharge, the effective value can reflect the overall energy level, and the kurtosis and skewness can characterize the distribution characteristics of the preprocessed high-frequency current pulse signal.

[0089] The third frequency domain feature: The preprocessed high-frequency current pulse signal is converted to the frequency domain through fast Fourier transform, and the center frequency, bandwidth and amplitude of each frequency component are extracted. Different discharge categories have different characteristic distributions in the frequency domain.

[0090] Third, time-frequency domain characteristics: Use time-frequency analysis methods (such as wavelet transform, short-time Fourier transform, etc.) to obtain the energy distribution characteristics of the preprocessed high-frequency current pulse signal on the time-frequency plane, such as the energy concentration area and change trend in the time-frequency diagram.

[0091] Second statistical feature: Calculate the statistics of the pre-processed high-frequency current pulse signal, such as probability density function and power spectrum density, and analyze its distribution law and statistical characteristics to distinguish different discharge categories.

[0092] S233: Inputting the third partial discharge feature into a pre-trained third classification model, and having the third classification model analyze the third partial discharge feature to determine a third discharge category, wherein the third discharge category is: internal discharge, floating potential discharge, or poor contact discharge.

[0093] Specifically, the third classification model is a classifier such as a support vector machine, a decision tree, a random forest, or a deep learning model such as a convolutional neural network, a recurrent neural network, etc.

[0094] S24: The first discharge category, the second discharge category, and the third discharge category are used as current discharge categories.

[0095] Specifically, the discharge situation of electrical equipment may be one type of discharge situation, or different types of discharge situations may exist simultaneously. For example, while the internal air gap in the insulation layer of a high-voltage cable causes internal discharge, surface discharge may occur at the terminal head of the high-voltage cable due to the concentrated electric field. In a humid and dirty environment, surface discharge is prone to occur on the surface of electrical equipment. At the same time, because humidity affects the insulation performance, internal discharge may occur inside the electrical equipment. The electric field distribution inside the power equipment is uneven, and corona discharge may occur in areas with high electric field strength. Internal discharge or surface discharge may also occur in weak insulation areas with severe electric field distortion.

[0096] S3: Analyze the discharge situation according to the current discharge category, device data, and environmental data to obtain a comprehensive discharge analysis result, wherein the comprehensive discharge analysis result is: normal, abnormal, or critical.

[0097] Furthermore, the discharge situation is analyzed based on the current discharge category, device data, and environmental data. The sub-steps for obtaining the comprehensive discharge analysis results are as follows:

[0098] S31: Read the current discharge category and randomly generate an analysis sequence number for each discharge category in the current discharge category. The analysis sequence numbers increase in a random order.

[0099] Specifically, the current discharge categories include A discharge categories, the analysis sequence number of the discharge category with a random order of 1 is 1, the analysis sequence number of the discharge category with a random order of 2 is 2, ..., and the analysis sequence number of the discharge category with a random order of A is A.

[0100] S32: The discharge category corresponding to the minimum value in the analysis sequence number is taken as the target discharge category, and the impact weight database is traversed according to the target discharge category to determine the impact weight data packet whose standard discharge category is consistent with the target discharge category as the target weight data packet, wherein the target weight data packet at least includes: discharge category hazard value, hazard degree weight, equipment factor weight, weight of each equipment indicator, environmental factor weight and weight of each environmental indicator.

[0101] S33: Calculate a target discharge evaluation value based on the target weight data packet, device data, and environmental data.

[0102] Furthermore, the expression of the target discharge evaluation value is:

[0103]

[0104] Among them, Spg i The target discharge evaluation value corresponding to the target discharge category with analysis number i; To analyze the hazard degree weight in the target weight data packet corresponding to the target discharge category with sequence number i; Fys i To analyze the discharge category hazard value in the target weight data packet corresponding to the target discharge category with sequence number i; To analyze the equipment factor weight in the target weight data packet corresponding to the target discharge category with sequence number i; Szb n is the normalized value of the nth device indicator in the device data; η n To analyze the weight of the nth device indicator in the target weight data packet corresponding to the target discharge category with sequence number i, n∈[1,N], N is a natural number, representing the total number of device indicators in the device data; μ i 3 To analyze the environmental factor weight in the target weight data packet corresponding to the target discharge category with sequence number i; Hzb m is the normalized value of the mth environmental indicator in the environmental data; m To analyze the weight of the mth environmental indicator in the target weight data packet corresponding to the target discharge category with serial number i, m∈[1,M], M is a natural number, representing the total number of environmental indicators in the environmental data.

[0105] Specifically, the nth device indicator in the device data is normalized by using existing technologies, and the nth device indicator is mapped to the interval [0, 10] to obtain the normalized value of the nth device indicator.

[0106] The mth environmental indicator in the environmental data is normalized by using existing technology, and the mth environmental indicator is mapped to the interval [0,10] to obtain the normalized value of the mth environmental indicator.

[0107] in, η n and λ m The specific value is set according to the actual situation.

[0108] S34: The analysis sequence number of the target discharge category is judged according to the total number of discharge categories in the current discharge category. If the total number of discharge categories in the current discharge category is greater than the analysis sequence number of the target discharge category, the analysis sequence number of the target discharge category is eliminated and S32 is executed; if the total number of discharge categories in the current discharge category is equal to the analysis sequence number of the target discharge category, the analysis sequence number of the target discharge category is eliminated and S35 is executed.

[0109] S35: Analyze all target discharge evaluation values ​​to obtain a comprehensive discharge analysis result.

[0110] Furthermore, all target discharge evaluation values ​​are analyzed to obtain comprehensive discharge analysis results in the following sub-steps:

[0111] S351: Traverse the evaluation threshold database according to the target discharge category corresponding to each target discharge evaluation value, and determine a standard threshold table whose standard discharge category is consistent with the target discharge category as the target threshold table.

[0112] S352: Use the target threshold table to analyze the corresponding target discharge evaluation value and generate a sub-discharge result. If the target discharge evaluation value is within the normal threshold range of the target threshold table, the generated sub-discharge result is normal; if the target discharge evaluation value is within the critical threshold range of the target threshold table, the generated sub-discharge result is critical; if the target discharge evaluation value is within the abnormal threshold range of the target threshold table, the generated sub-discharge result is abnormal.

[0113] S353: Generate a comprehensive discharge analysis result based on all sub-discharge results; if all sub-discharge results are normal, the generated comprehensive discharge analysis result is normal; if there are one or more abnormalities in all sub-discharge results, the generated comprehensive discharge analysis result is abnormal; if there are one or more critical results in all sub-discharge results and no abnormalities, the generated comprehensive discharge analysis result is critical.

[0114] S4: When the comprehensive discharge analysis result is normal, send the comprehensive discharge analysis result; when the comprehensive discharge analysis result is critical, send the comprehensive discharge analysis result, obtain the adjusted monitoring frequency, and use the adjusted monitoring frequency as the preset monitoring frequency to execute S1; when the comprehensive discharge analysis result is abnormal, obtain maintenance data, and send the comprehensive discharge analysis result and maintenance data, wherein the maintenance data at least includes: discharge location, current discharge category, and risk index corresponding to each discharge category in the current discharge category.

[0115] Specifically, the discharge position is obtained using existing technologies. For example, ultra-high frequency electromagnetic wave signals can use the time difference positioning method or array signal processing method to obtain the discharge position; ultrasonic signals can use the triangulation positioning method or acoustic imaging method to obtain the discharge position; high-frequency current pulse signals can use the traveling wave method or current transformer method to obtain the discharge position.

[0116] The risk index corresponding to each discharge category in the current discharge category is: the risk level corresponding to the risk level range to which the target discharge assessment value of the discharge category belongs.

[0117] Furthermore, the sub-steps for obtaining the adjusted monitoring frequency are as follows:

[0118] S41: Acquire historical monitoring data, and acquire a frequency index value according to the historical monitoring data and a target discharge evaluation value, wherein the historical monitoring data includes: a total number of current monitoring times and a total number of current abnormal times.

[0119]

[0120] Among them, Psyz is the frequency index value; Gjc is the total number of current abnormalities; Zjc is the total number of current monitoring times; To analyze the hazard degree weight in the target weight data packet corresponding to the target discharge category with sequence number i; Spg i To analyze the target discharge evaluation value corresponding to the target discharge category with serial number i, i∈[1,I], I is the total number of target discharge evaluation values, and I is a natural number.

[0121] S42: Traverse the monitoring frequency data table according to the frequency index value, and determine that the adjustment frequency value corresponding to the frequency index range to which the frequency index value belongs is the target adjustment frequency value.

[0122] S43: Obtaining an adjusted monitoring frequency according to the target adjustment frequency value and the preset monitoring frequency, where the adjusted monitoring frequency = the preset monitoring frequency + the target adjustment frequency value.

[0123] Specifically, the preset monitoring frequency is the number of times the comprehensive monitoring data is acquired within a unit of time, and the target adjustment frequency value is the number of times the comprehensive monitoring data needs to be acquired within a unit of time.

[0124] When the comprehensive discharge analysis result is critical, timely adjusting the monitoring frequency according to the degree of hazard of historical abnormal conditions and target discharge assessment value can further improve the real-time and accuracy of monitoring.

[0125] Furthermore, when the comprehensive discharge analysis result is critical, the method further includes: obtaining and sending predicted discharge data, wherein the predicted discharge data includes: multiple predicted discharge indicators, such as: discharge amount, discharge intensity, discharge frequency, discharge phase and discharge energy.

[0126] Furthermore, the expression for predicting discharge index is:

[0127]

[0128] Among them, Yfd k is the kth predicted discharge index; β0, β e , β r , β v All are regression coefficients; Xsb e is the e-th type of data in the equipment historical data, e∈[1,E], E is the total type of data in the equipment historical data required to calculate the k-th predicted discharge index; Xhj r is the rth indicator in the environmental factor data, r∈[1,R], R is the total number of indicators in the environmental factor data involved in calculating the kth predicted discharge indicator; Xlb v is the discharge characteristic parameter of the corresponding discharge category, v∈[1,V], V is the total amount of discharge characteristic parameters of the discharge category involved in calculating the kth predicted discharge index; τ is the error term.

[0129] Specifically, the regression coefficient and error term are set based on actual conditions. Predicted discharge data can help users predict the time and location of potential equipment failures, facilitating early maintenance planning and avoiding serious consequences such as production interruptions and safety incidents caused by sudden equipment failures. This allows for timely maintenance and repairs before serious damage occurs, minimizing wear and aging caused by excessive or abnormal discharge, and extending equipment lifespan.

[0130] The equipment historical data includes at least equipment voltage historical data, equipment current historical data, equipment power historical data, equipment discharge amount historical data, equipment discharge frequency historical data, equipment discharge pulse width historical data, equipment temperature historical data, equipment insulation resistance historical data and equipment operating time historical data.

[0131] The indicators in the environmental factor data include at least: temperature, humidity, air pressure, pollution level, altitude and electromagnetic interference intensity.

[0132] The discharge characteristic parameters include at least discharge pulse characteristic parameters (eg pulse amplitude), discharge statistical characteristic parameters (eg discharge repetition rate) and spectrum characteristic parameters (eg frequency bandwidth).

[0133] The beneficial effects achieved by this application are as follows:

[0134] (1) The comprehensive monitoring and analysis method and system for partial discharge data of power equipment of the present application can monitor and analyze a variety of discharge data, and the analysis results are comprehensive and accurate.

[0135] (2) The comprehensive monitoring and analysis method and system for partial discharge data of power equipment of the present application fully considers the impact of different discharge categories, equipment data and environmental data on the analysis results of partial discharge conditions, effectively improving the comprehensiveness and accuracy of the analysis.

[0136] (3) The comprehensive monitoring and analysis method and system of partial discharge data of power equipment of the present application can timely adjust the monitoring frequency according to the real-time situation, thereby ensuring the real-time and accuracy of the monitoring.

[0137] (4) The comprehensive monitoring and analysis method and system of partial discharge data of power equipment of the present application can obtain predicted discharge data when the comprehensive discharge analysis result is critical. The predicted discharge data can assist the user terminal to know in advance the time and location of possible equipment failure, so as to facilitate the arrangement of maintenance plans in advance, thereby avoiding serious consequences such as production interruption and safety accidents caused by sudden equipment failure; and can timely perform maintenance and repair on the equipment before it is seriously damaged, thereby reducing the wear and aging of the equipment caused by excessive discharge or abnormal discharge, and extending the service life of the equipment.

[0138] Although preferred embodiments of the present application have been described, those skilled in the art may make additional changes and modifications to these embodiments once they become aware of the underlying inventive concepts. Therefore, the scope of protection of this application is intended to include the preferred embodiments and all changes and modifications that fall within the scope of this application. Obviously, those skilled in the art may make various changes and modifications to this application without departing from the spirit and scope of this application. Thus, if such changes and modifications of this application fall within the scope of protection of this application and its equivalents, then this application is intended to include such changes and modifications.

Claims

1. A comprehensive monitoring and analysis method for partial discharge data of power equipment, characterized in that: The steps include: S1: Obtaining comprehensive monitoring data according to a preset monitoring frequency, wherein the comprehensive monitoring data at least includes: original signals, equipment data, and environmental data; S2: Classifying and processing the original signals in the comprehensive monitoring data to determine the current discharge category, wherein the current discharge category includes at least one or more of internal discharge, corona discharge, creeping discharge, floating potential discharge, and poor contact discharge; S3: Analyze the discharge situation based on the current discharge category, device data, and environmental data to obtain a comprehensive discharge analysis result, where the comprehensive discharge analysis result is: normal, abnormal, or critical; S4: When the comprehensive discharge analysis result is normal, the comprehensive discharge analysis result is sent; when the comprehensive discharge analysis result is critical, the comprehensive discharge analysis result is sent, and the adjusted monitoring frequency is obtained, and the adjusted monitoring frequency is used as the preset monitoring frequency, and S1 is executed; when the comprehensive discharge analysis result is abnormal, maintenance data is obtained, and the comprehensive discharge analysis result and maintenance data are sent, wherein the maintenance data at least includes: discharge location, current discharge category, and risk index corresponding to each discharge category in the current discharge category; The sub-steps for obtaining the adjusted monitoring frequency are as follows: S41: Acquire historical monitoring data, and obtain a frequency index value based on the historical monitoring data and a target discharge evaluation value, wherein the historical monitoring data includes: the total number of current monitoring times and the total number of current abnormalities; the target discharge evaluation value is used to determine the sub-discharge result of the corresponding target discharge category; S42: Traverse the monitoring frequency data table according to the frequency index value, and determine the target adjustment frequency value corresponding to the frequency index range to which the frequency index value belongs; S43: Obtaining an adjusted monitoring frequency according to the target adjustment frequency value and the preset monitoring frequency, where the adjusted monitoring frequency = the preset monitoring frequency + the target adjustment frequency value; in, ; in, is the frequency index value; The total number of current exceptions; The total number of current monitoring times; For analysis number The hazard level weight in the target weight data packet corresponding to the target discharge category; For analysis number The target discharge evaluation value corresponding to the target discharge category, , is the total number of target discharge evaluation values, is a natural number.

2. The comprehensive monitoring and analysis method for partial discharge data of power equipment according to claim 1, characterized in that: The sub-steps for classifying the original signals in the comprehensive monitoring data and determining the current discharge category are as follows: S21: Classify and process the ultra-high frequency electromagnetic wave signal in the original signal to determine a first discharge category; S22: classifying the ultrasonic signal in the original signal to determine a second discharge category; S23: classifying the high-frequency current pulse signal in the original signal to determine a third discharge category; S24: The first discharge category, the second discharge category, and the third discharge category are used as current discharge categories.

3. The comprehensive monitoring and analysis method for partial discharge data of power equipment according to claim 2, characterized in that: The sub-steps of classifying the UHF electromagnetic wave signal in the original signal and determining the first discharge category are as follows: S211: performing a first preprocessing on the ultra-high frequency electromagnetic wave signal in the original signal to obtain a preprocessed ultra-high frequency electromagnetic wave signal, wherein the first preprocessing at least includes: noise reduction processing and filtering processing; S212: extracting features from the preprocessed ultra-high frequency electromagnetic wave signal to obtain a first partial discharge feature, wherein the first partial discharge feature includes: a first time domain feature, a first frequency domain feature, a first time-frequency domain feature, and a statistical feature; S213: Inputting the first partial discharge feature into a pre-trained first classification model, and having the first classification model analyze the first partial discharge feature to determine a first discharge category, wherein the first discharge category is: internal discharge, corona discharge, or creeping discharge.

4. The comprehensive monitoring and analysis method for partial discharge data of power equipment according to claim 2, characterized in that: The sub-steps of classifying the ultrasonic signal in the original signal and determining the second discharge category are as follows: S221: performing a second preprocessing on the ultrasonic signal in the original signal to obtain a preprocessed ultrasonic signal, wherein the second preprocessing at least includes: denoising processing and normalization processing; S222: extracting features from the preprocessed ultrasonic signal to obtain a second partial discharge feature, wherein the second partial discharge feature includes: a second time domain feature, a second frequency domain feature, and a second time-frequency domain feature; S223: Inputting the second partial discharge feature into a pre-trained second classification model, and having the second classification model analyze the second partial discharge feature to determine a second discharge category, wherein the second discharge category is: internal discharge, surface discharge, or corona discharge.

5. The comprehensive monitoring and analysis method for partial discharge data of power equipment according to claim 2, characterized in that: The sub-steps of classifying the high-frequency current pulse signal in the original signal and determining the third discharge category are as follows: S231: performing a third preprocessing on the high-frequency current pulse signal in the original signal to obtain a preprocessed high-frequency current pulse signal, wherein the third preprocessing at least includes: filtering processing and normalization processing; S232: extracting features from the preprocessed high-frequency current pulse signal to obtain a third partial discharge feature, wherein the third partial discharge feature includes: a third time domain feature, a third frequency domain feature, a third time-frequency domain feature, and a second statistical feature; S233: Inputting the third partial discharge feature into a pre-trained third classification model, and having the third classification model analyze the third partial discharge feature to determine a third discharge category, wherein the third discharge category is: internal discharge, floating potential discharge, or poor contact discharge.

6. The comprehensive monitoring and analysis method for partial discharge data of power equipment according to claim 1, characterized in that: The sub-steps for analyzing the discharge situation based on the current discharge category, device data, and environmental data to obtain the comprehensive discharge analysis results are as follows: S31: Read the current discharge category and randomly generate an analysis sequence number for each discharge category in the current discharge category, and the analysis sequence number increases in a random order; S32: The discharge category corresponding to the minimum value in the analysis sequence number is used as the target discharge category. The impact weight database is traversed according to the target discharge category to determine the impact weight data package whose standard discharge category is consistent with the target discharge category as the target weight data package, wherein the target weight data package at least includes: the discharge category hazard value, the hazard degree weight, the equipment factor weight, the weight of each equipment indicator, the environmental factor weight, and the weight of each environmental indicator; S33: Calculating a target discharge evaluation value based on the target weight data packet, device data, and environmental data; S34: judging the analysis sequence number of the target discharge category based on the total number of discharge categories in the current discharge category. If the total number of discharge categories in the current discharge category is greater than the analysis sequence number of the target discharge category, the analysis sequence number of the target discharge category is eliminated and S32 is executed. If the total number of discharge categories in the current discharge category is equal to the analysis sequence number of the target discharge category, the analysis sequence number of the target discharge category is eliminated and S35 is executed. S35: Analyze all target discharge evaluation values ​​to obtain a comprehensive discharge analysis result.

7. The comprehensive monitoring and analysis method for partial discharge data of power equipment according to claim 6, characterized in that: The expression of the target discharge evaluation value is: ； in, For analysis number The target discharge evaluation value corresponding to the target discharge category; For analysis number The hazard level weight in the target weight data packet corresponding to the target discharge category; For analysis number The discharge category hazard value in the target weight data packet corresponding to the target discharge category; For analysis number The device factor weight in the target weight data package corresponding to the target discharge category; The first Normalized value of each device indicator; For analysis number The target weight data packet corresponding to the target discharge category The weight of each device indicator, , It is a natural number, indicating the total number of device indicators in the device data; For analysis number The environmental factor weights in the target weight data package corresponding to the target discharge category; The environmental data Normalized values ​​of environmental indicators; For analysis number The target weight data packet corresponding to the target discharge category The weight of each environmental indicator, , It is a natural number, indicating the total number of environmental indicators in the environmental data.

8. The comprehensive monitoring and analysis method for partial discharge data of power equipment according to claim 6, characterized in that: The sub-steps for analyzing all target discharge evaluation values ​​and obtaining comprehensive discharge analysis results are as follows: S351: Traverse the evaluation threshold database according to the target discharge category corresponding to each target discharge evaluation value, and determine a standard threshold table whose standard discharge category is consistent with the target discharge category as the target threshold table; S352: Analyze the corresponding target discharge evaluation value using the target threshold table to generate a sub-discharge result. If the target discharge evaluation value is within the normal threshold range of the target threshold table, the generated sub-discharge result is normal. If the target discharge evaluation value is within the critical threshold range of the target threshold table, the generated sub-discharge result is critical; if the target discharge evaluation value is within the abnormal threshold range of the target threshold table, the generated sub-discharge result is abnormal; S353: Generate a comprehensive discharge analysis result based on all sub-discharge results; if all sub-discharge results are normal, the generated comprehensive discharge analysis result is normal; If there are one or more anomalies in all sub-discharge results, the generated comprehensive discharge analysis result is abnormal; If one or more of all sub-discharge results are critical and no abnormalities exist, the comprehensive discharge analysis result is critical.

9. A comprehensive monitoring and analysis system for partial discharge data of power equipment, characterized in that: include: multiple user terminals and an integrated monitoring and analysis center; Among them, the user terminal is used to send comprehensive monitoring data to the comprehensive monitoring and analysis center according to the preset monitoring frequency; receive comprehensive discharge analysis results and maintenance data; Comprehensive monitoring and analysis center: used to execute the comprehensive monitoring and analysis method of partial discharge data of power equipment as described in any one of claims 1-8.

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