High-voltage switchgear fault diagnosis method and system based on deep learning

Abstract

The present invention provides a high-voltage switchgear fault diagnosis method and system based on deep learning. The method divides the switchgear into several regions based on its operating functions, collects operating data from the regions based on the partitioning results, and divides the operating data of each region based on the public operating conditions of each region. A switchgear simulation model is constructed, and the operating data of each region is input into the simulation model for simulation training to obtain simulation result data. The simulation result data is then compared with the operating data to obtain switchgear fault information. A data set is constructed based on the fault information, and a neural network model is constructed. The deep learning model is trained using the fault information to obtain a fault diagnosis model. The method achieves the purpose of inferring the influence coefficient of abnormal information in different regions on the fault based on the impact of the fault on different regions, further ensuring the accuracy of switchgear fault diagnosis.

Description

Technical Field

[0001] The present invention relates to the technical field of switchgear fault diagnosis, and in particular to a high-voltage switchgear fault diagnosis method and system based on deep learning. Background Art

[0002] High-voltage switchgear is a critical component of power systems, used to control, protect, and distribute electrical energy. Its proper operation is crucial to the stability, reliability, and safety of power systems. However, due to long-term operation, environmental factors, and improper operation, high-voltage switchgear is prone to various faults, such as insulation degradation, poor contact, partial discharge, and mechanical failure. These faults not only affect the normal operation of power systems but can also lead to serious safety incidents.

[0003] Currently, fault diagnosis of high-voltage switchgear mainly relies on regular inspections, offline testing, and simple online monitoring. These methods have the following limitations:

[0004] Regular inspections: Rely on manual experience, are inefficient, and make it difficult to detect early faults.

[0005] Offline testing: requires power outage, affecting the normal operation of the power system.

[0006] Simple online monitoring: Usually only a single parameter (such as temperature, current) can be monitored, which makes it difficult to fully reflect the operating status of the switchgear.

[0007] Insufficient data analysis: There is a lack of fusion and in-depth analysis of multi-source data, resulting in low accuracy and timeliness of fault diagnosis.

[0008] Therefore, there is an urgent need for a method for accurately diagnosing switchgear faults. Summary of the Invention

[0009] In response to the shortcomings of the existing technology, the present invention provides a high-voltage switchgear fault diagnosis method and system based on deep learning to solve the problems raised in the above background technology.

[0010] To achieve the above objectives, the present invention is implemented through the following technical solutions: A high-voltage switchgear fault diagnosis method based on deep learning, comprising the following steps:

[0011] The switchgear is divided into several areas based on its operating functions, and the operating data of several areas are collected based on the partitioning results;

[0012] Among them, the operating data includes independent variable data and dependent variable data;

[0013] The operating data of each area is divided based on the public operation conditions of each area;

[0014] Build a switchgear simulation model, input the operating data of each area of the switchgear into the simulation model for simulation training, obtain simulation result data, and obtain switchgear fault information by comparing the simulation result data with the operating data;

[0015] Building a data set based on the fault information, building a neural network model, and training the deep learning model through the fault information to obtain a fault diagnosis model;

[0016] The operating data of the switchgear is input into the fault diagnosis model to obtain the fault diagnosis results of the target switchgear.

[0017] As a preferred embodiment of this invention, the method for establishing the switch cabinet simulation model includes:

[0018] Establish a physical model of the switchgear to ensure that the switchgear model parameters are consistent with the switchgear reagent properties;

[0019] Set the switch cabinet model environmental parameters according to the switch cabinet working environment;

[0020] After setting all parameters, simulation calculations are performed to calculate the operating parameters and operating status of the switchgear. After the calculations are completed, the simulation results are analyzed and evaluated.

[0021] As a preferred embodiment of this embodiment, the comparing simulation result data and operation data to obtain switch cabinet fault information includes:

[0022] Arrange and identify the simulation result data and the independent variable data in the operation data of each area in time sequence and correspond them;

[0023] Extract key parameters from simulation result data and regional operation data;

[0024] Compare the key parameters of the independent variable data corresponding to the dependent variable data in the regional operation data with the key parameters of the corresponding simulation result data;

[0025] Determine the fault node, analyze the key parameter characteristics of other areas based on the timing characteristics of the fault node, determine the fault type corresponding to the fault node, and obtain fault information.

[0026] As a preferred embodiment of this invention, the process of constructing a data set based on the fault information, constructing a deep learning model, and training the deep learning model using the fault information includes:

[0027] Extracting characteristic data from fault data to obtain a characteristic data set;

[0028] Divide the feature dataset into training set and validation set;

[0029] Build a deep learning model and input the training set into the deep learning model for training;

[0030] The trained model is validated using the validation set. Based on the distribution of the number of switchgear areas identified as faulty in the validation records, all validation records for each area are categorized. Regional combinations that can influence fault diagnosis are extracted, and the impact index of these regional combinations on the deep learning model's fault diagnosis is evaluated.

[0031] The deep learning model is optimized based on the distribution of regions in the regional combination whose fault diagnosis impact index is greater than the preset threshold.

[0032] As a preferred embodiment of this invention, the step of evaluating the fault diagnosis impact index of the region combination on the deep learning model specifically includes:

[0033] Obtain verification records where only a certain area is a fault area and determine it as fault a;

[0034] Obtain a region combination that has been determined to be fault a at least once in the verification record, where the region combination set is M and the total number of regions in the region combination is I, and determine a set m in the set M that affects fault a;

[0035] Obtain all region combinations in the verification record that are judged to be fault a and whose set contains M. The total number of regions in all region combinations is J;

[0036] Calculate the characteristic influencing factors of the fault area of fault a on the fault diagnosis process of the deep learning model:

[0037] ;

[0038] Where γ represents the correction coefficient;

[0039] Evaluation of the impact index of regional combinations on the fault diagnosis of deep learning models:

[0040] ;

[0041] in, They represent the 1st, 2nd, 3rd, ..., ith areas in the area combination of fault a, and i>2.

[0042] Another aspect of the present invention provides a high-voltage switchgear fault diagnosis system based on deep learning, which is used to implement the above-mentioned high-voltage switchgear fault diagnosis method based on deep learning, including:

[0043] The area division module divides the switchgear into several areas based on its operating functions and collects the operating data of several areas based on the partitioning results;

[0044] Among them, the operating data includes independent variable data and dependent variable data;

[0045] The region division module is further used to divide the operation data of each region based on the operation conditions of each region;

[0046] A model building module is used to build a switchgear simulation model, input the operating data of each area of the switchgear into the simulation model for simulation training, obtain simulation result data, and obtain switchgear fault information by comparing the simulation result data with the operating data; it is also used to build a data set based on the fault information, build a neural network model, and train the deep learning model with the fault information to obtain a fault diagnosis model;

[0047] The fault diagnosis module is used to input the operating data of the switch cabinet into the fault diagnosis model to obtain the fault diagnosis result of the target switch cabinet.

[0048] As a preferred embodiment of this invention, the method for establishing the switch cabinet simulation model includes:

[0049] Establish a physical model of the switchgear to ensure that the switchgear model parameters are consistent with the switchgear reagent properties;

[0050] Set the switch cabinet model environmental parameters according to the switch cabinet working environment;

[0051] After setting all parameters, simulation calculations are performed to calculate the operating parameters and operating status of the switchgear. After the calculations are completed, the simulation results are analyzed and evaluated.

[0052] As a preferred embodiment of this embodiment, the comparing simulation result data and operation data to obtain switch cabinet fault information includes:

[0053] Arrange and identify the simulation result data and the independent variable data in the operation data of each area in time sequence and correspond them;

[0054] Extract key parameters from simulation result data and regional operation data;

[0055] Compare the key parameters of the independent variable data corresponding to the dependent variable data in the regional operation data with the key parameters of the corresponding simulation result data;

[0056] Determine the fault node, analyze the key parameter characteristics of other areas based on the timing characteristics of the fault node, determine the fault type corresponding to the fault node, and obtain fault information.

[0057] As a preferred embodiment of this invention, the process of constructing a data set based on the fault information, constructing a deep learning model, and training the deep learning model using the fault information includes:

[0058] Extracting characteristic data from fault data to obtain a characteristic data set;

[0059] Divide the feature dataset into training set and validation set;

[0060] Build a deep learning model and input the training set into the deep learning model for training;

[0061] The trained model is validated using the validation set. Based on the distribution of the number of switchgear areas identified as faulty in the validation records, all validation records for each area are categorized. Regional combinations that can influence fault diagnosis are extracted, and the impact index of these regional combinations on the deep learning model's fault diagnosis is evaluated.

[0062] The deep learning model is optimized based on the distribution of regions in the regional combination whose fault diagnosis impact index is greater than the preset threshold.

[0063] As a preferred embodiment of this invention, the step of evaluating the fault diagnosis impact index of the region combination on the deep learning model specifically includes:

[0064] Obtain verification records where only a certain area is a fault area and determine it as fault a;

[0065] Obtain a region combination that has been determined to be fault a at least once in the verification record, where the region combination set is M and the total number of regions in the region combination is I, and determine a set m in the set M that affects fault a;

[0066] Obtain all region combinations in the verification record that are judged to be fault a and whose set contains M. The total number of regions in all region combinations is J;

[0067] Calculate the characteristic influencing factors of the fault area of fault a on the fault diagnosis process of the deep learning model:

[0068] ;

[0069] Where γ represents the correction coefficient;

[0070] Evaluation of the impact index of regional combinations on the fault diagnosis of deep learning models:

[0071] ;

[0072] in, They represent the 1st, 2nd, 3rd, ..., ith areas in the area combination of fault a, and i>2.

[0073] The present invention provides a high-voltage switchgear fault diagnosis method and system based on deep learning, which has the following beneficial effects: by dividing the switchgear into areas, and further dividing the areas of the switchgear according to different working conditions, and using the method of establishing a simulation model, simulation models are performed for different working conditions in different areas of the switchgear, based on the comparison of the simulation results and the actual operation data, abnormal data is obtained through analysis to ensure the authenticity and accuracy of the data, and the deep learning model is trained and verified by the obtained abnormal data, and in the verification records, the number distribution of areas in the switchgear that are judged to be faulty is obtained, all verification records of each area are classified respectively, and regional combinations composed of different areas that can affect the fault diagnosis of the model are extracted, and the fault diagnosis influence index of the regional combination on the deep learning model is evaluated, so as to realize the influence coefficient of abnormal information of different areas on the fault based on the influence of the fault on different areas, thereby further ensuring the accuracy of the switchgear fault judgment. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] Figure 1 This is a flow chart of the high-voltage switchgear fault diagnosis method based on deep learning of the present invention;

[0075] Figure 2 This is a block diagram of the high-voltage switchgear fault diagnosis system based on deep learning of the present invention. DETAILED DESCRIPTION

[0076] The embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and are not to be construed as limiting the present invention.

[0077] The disclosure below provides many different embodiments or examples for realizing different structures of the present invention. In order to simplify the disclosure of the present invention, the components and settings of specific examples are described below. Of course, they are merely examples and are not intended to limit the present invention. In addition, the present invention may repeat reference numbers and / or reference letters in different examples. Such repetition is for the purpose of simplicity and clarity and does not in itself indicate the relationship between the various embodiments and / or settings discussed. In addition, the present invention provides examples of various specific processes and materials, but those skilled in the art will recognize the application of other processes and / or the use of other materials.

[0078] like Figure 1 As shown, an embodiment of the present invention provides a high-voltage switchgear fault diagnosis method based on deep learning, comprising the following steps:

[0079] S1: The switchgear is divided into several areas based on its operating functions, and the operating data of several areas are collected based on the partitioning results;

[0080] Among them, the operating data includes independent variable data and dependent variable data;

[0081] In this embodiment, several areas are divided according to operating functions, including:

[0082] Incoming line area: used to connect external power supply or upper power grid to introduce electrical energy into the switch cabinet;

[0083] Busbar area: As the core part of power distribution, it connects the incoming line area and the outgoing line area to realize the collection and distribution of power;

[0084] Outgoing line area: distributes electrical energy from the switchgear to downstream equipment or loads;

[0085] Protection and control area: realize monitoring, protection and control of the operating status of the switchgear;

[0086] Grounding and safety zone: ensure safe operation of equipment and prevent overvoltage and leakage accidents;

[0087] Auxiliary equipment area: provides auxiliary support for the switch cabinet, such as power supply, communication and heat dissipation;

[0088] Isolation and maintenance area: used for equipment isolation and maintenance to ensure the safety of operators;

[0089] Eight regions were derived based on executive function.

[0090] Among them, the operation specifically includes current data, temperature data, humidity data, vibration frequency data and historical maintenance and repair data, as well as environmental physical quantities such as humidity and temperature inside and outside the switch cabinet.

[0091] S2: Divide the operating data of each area based on the public operation conditions of each area;

[0092] It can be understood that according to the operating status classification, the operating status of the switchgear can be divided into normal working conditions, load working conditions and standby working conditions.

[0093] S3: Build a switchgear simulation model, input the operating data of each area of the switchgear into the simulation model for simulation training, obtain simulation result data, and obtain switchgear fault information by comparing the simulation result data with the operating data;

[0094] The fault information includes fault information of different operating conditions in each area and the relationship between the fault information and each area;

[0095] In this embodiment, the switch cabinet simulation model is established in the following manner:

[0096] Establish a physical model of the switchgear to ensure that the switchgear model parameters are consistent with the switchgear reagent properties;

[0097] Set the switch cabinet model environmental parameters according to the switch cabinet working environment;

[0098] After setting all parameters, simulation calculations are performed to calculate the operating parameters and operating status of the switchgear. After the calculations are completed, the simulation results are analyzed and evaluated.

[0099] Among them, by comparing the simulation result data and the operation data, the switch cabinet fault information obtained includes:

[0100] Arrange and identify the simulation result data and the independent variable data in the operation data of each area in time sequence and correspond them;

[0101] Extract key parameters from simulation result data and regional operation data;

[0102] Compare the key parameters of the independent variable data corresponding to the dependent variable data in the regional operation data with the key parameters of the corresponding simulation result data;

[0103] Determine the fault node, analyze the key parameter characteristics of other areas based on the timing characteristics of the fault node, determine the fault type corresponding to the fault node, and obtain fault information.

[0104] Specifically, the fault node can be a single-region fault. By determining the fault nodes of key parameters in other regions of the time series, the changes in key parameters in other regions after the single-region fault can be determined, that is, the correlation of regional faults can be determined.

[0105] It is understandable that the independent variable data of the switchgear include temperature data (the temperature of key parts inside the switchgear such as busbars, circuit breakers, cable connectors, etc.), current data (the current value passing through the switchgear, reflecting the load conditions), voltage data (input and output voltages of the switchgear), vibration data (vibration signals of mechanical components inside the switchgear such as circuit breakers and disconnectors). The dependent variable data include ambient temperature (the temperature of the environment where the switchgear is located, which affects the internal temperature), ambient humidity (the humidity of the environment where the switchgear is located, which affects the internal humidity), load changes (load changes in the power system affect the current and voltage of the switchgear), and external vibrations (such as vibrations caused by nearby equipment or traffic, which may affect the mechanical components of the switchgear).

[0106] In this embodiment, by dividing the switch cabinet into different areas according to function and further analyzing different areas according to different working conditions, refined control of the switch cabinet is achieved. At the same time, by constructing a simulation model of the switch cabinet, the working parameter performance of the switch cabinet under different working conditions can be accurately represented, so that in-depth analysis of different areas can be carried out to improve the accuracy of fault diagnosis. Based on the analysis of regional faults, the correlation of key data of each area can be further analyzed, which can further improve the accuracy of switch cabinet fault analysis.

[0107] Among them, in order to realize the diagnosis of fault points in different areas and different working conditions of the switch cabinet, the operating data of the switch cabinet at the preset periodic time is first collected, the switch cabinet is classified into areas, and further refined classification is performed based on the operating conditions. The fault point can be independent or caused by the mutual influence of each area. By simulating the switch cabinet, the operating conditions of different areas of the switch cabinet are simulated based on the independent variable data, and the independent faults in different areas of the switch cabinet are reflected. The operating data of other areas under the time series can be used to determine the impact of independent faults on other areas. The impact of a certain fault on different areas can be screened out, that is, the important characteristics of the fault point can be diagnosed. Conversely, these important characteristics can be used to diagnose the specific cause of the fault point, and accurate diagnosis and identification of the specific location of the switch cabinet fault point can be achieved.

[0108] In other embodiments, by identifying the operating data immediately before the fault occurs and analyzing the switch cabinet operating data, the location of the switch cabinet fault can be predicted in advance, so that maintenance can be performed in advance.

[0109] S4: Build a data set based on the fault information, construct a neural network model, and train the deep learning model with the fault information to obtain a fault diagnosis model;

[0110] In this embodiment, the process of constructing a data set based on fault information, constructing a deep learning model, and training the deep learning model using the fault information includes:

[0111] S41: extracting feature data from the fault data to obtain a feature data set;

[0112] S42: Divide the feature dataset into a training set and a validation set;

[0113] S43: Build a deep learning model and input the training set into the deep learning model for training;

[0114] S44: Validate the trained model using the validation set. Based on the distribution of the number of regions in the switchgear that were identified as faulty in the validation records, classify all validation records for each region. Extract region combinations that can influence the model's fault diagnosis and evaluate the impact index of these region combinations on the deep learning model's fault diagnosis.

[0115] S45: Optimize the deep learning model based on the distribution of regions in the regional combination whose fault diagnosis impact index is greater than a preset threshold.

[0116] The steps of S44 specifically include:

[0117] Obtain verification records where only a certain area is a fault area and determine it as fault a;

[0118] Obtain the region combination determined to be fault a at least once in the verification record, where the set of region combinations is M, the total number of regions in the region combination is I, and determine the set m of regions that affect fault a in the set M;

[0119] Obtain all region combinations in the verification record that are judged to be fault a and whose set contains M. The total number of regions in all region combinations is J;

[0120] Calculate the characteristic influencing factors of the fault area of fault a on the fault diagnosis process of the deep learning model:

[0121] ;

[0122] Where γ represents the correction coefficient;

[0123] Evaluation of the impact index of regional combinations on the fault diagnosis of deep learning models:

[0124] ;

[0125] in, They represent the 1st, 2nd, 3rd, ..., ith areas in the area combination of fault a, and i>2.

[0126] It should be noted that before extracting characteristic data from fault data, fault data processing is also included. The processing process includes data cleaning and noise removal. The specific process is an existing technical means and will not be described in detail here.

[0127] S5: Input the operating data of the switchgear into the fault diagnosis model to obtain the fault diagnosis result of the target switchgear.

[0128] The present invention divides the switch cabinet into areas, and further divides the areas of the switch cabinet according to different working conditions. It uses the method of establishing a simulation model to perform simulation models for different working conditions in different areas of the switch cabinet. The simulation results are compared with the actual operation data, and abnormal data is obtained through analysis to ensure the authenticity and accuracy of the data. At the same time, the deep learning model is trained and verified by the obtained abnormal data. In the verification records, the number distribution of areas in the switch cabinet that are judged to be faulty is obtained, and all verification records of each area are classified respectively. The regional combination composed of different areas that can affect the fault diagnosis of the model is extracted, and the fault diagnosis influence index of the regional combination on the deep learning model is evaluated. Based on the impact of the fault on different areas, the influence coefficient of the abnormal information of different areas on the fault is reversed, thereby further ensuring the accuracy of the switch cabinet fault judgment.

[0129] like Figure 2 As shown, this embodiment further provides a high-voltage switchgear fault diagnosis system based on deep learning, which is used to implement the above-mentioned high-voltage switchgear fault diagnosis method based on deep learning, including:

[0130] The area division module divides the switchgear into several areas based on its operating functions and collects the operating data of several areas based on the partitioning results;

[0131] Among them, the operating data includes independent variable data and dependent variable data;

[0132] The regional division module is also used to divide the operating data of each area based on the operating conditions of each area;

[0133] The model building module constructs a switchgear simulation model, inputs the operating data of each area of the switchgear into the simulation model for simulation training, obtains simulation result data, and obtains switchgear fault information by comparing the simulation result data with the operating data. It is also used to build a data set based on the fault information, build a neural network model, and train the deep learning model with the fault information to obtain a fault diagnosis model.

[0134] The fault diagnosis module is used to input the operating data of the switch cabinet into the fault diagnosis model to obtain the fault diagnosis result of the target switch cabinet.

[0135] Furthermore, the method for establishing the switchgear simulation model includes:

[0136] Establish a physical model of the switchgear to ensure that the switchgear model parameters are consistent with the switchgear reagent properties;

[0137] Set the switch cabinet model environmental parameters according to the switch cabinet working environment;

[0138] After setting all parameters, simulation calculations are performed to calculate the operating parameters and operating status of the switchgear. After the calculations are completed, the simulation results are analyzed and evaluated.

[0139] Furthermore, by comparing the simulation result data with the operating data, the switchgear fault information obtained includes:

[0140] Arrange and identify the simulation result data and the independent variable data in the operation data of each area in time sequence and correspond them;

[0141] Extract key parameters from simulation result data and regional operation data;

[0142] Compare the key parameters of the independent variable data corresponding to the dependent variable data in the regional operation data with the key parameters of the corresponding simulation result data;

[0143] Determine the fault node, analyze the key parameter characteristics of other areas based on the timing characteristics of the fault node, determine the fault type corresponding to the fault node, and obtain fault information.

[0144] Furthermore, the process of constructing a data set based on the fault information, building a deep learning model, and training the deep learning model using the fault information includes:

[0145] Extracting characteristic data from fault data to obtain a characteristic data set;

[0146] Divide the feature dataset into training set and validation set;

[0147] Build a deep learning model and input the training set into the deep learning model for training;

[0148] The trained model is validated using the validation set. Based on the distribution of the number of switchgear areas identified as faulty in the validation records, all validation records for each area are categorized. Regional combinations that can influence the model's fault diagnosis are extracted, and the impact index of these regional combinations on the deep learning model's fault diagnosis is evaluated.

[0149] The deep learning model is optimized based on the distribution of regions in the regional combination whose fault diagnosis impact index is greater than the preset threshold.

[0150] Furthermore, the steps of evaluating the impact index of the regional combination on the fault diagnosis of the deep learning model specifically include:

[0151] Obtain verification records where only a certain area is a fault area and determine it as fault a;

[0152] Obtain the region combination determined to be fault a at least once in the verification record, where the set of region combinations is M, the total number of regions in the region combination is I, and determine the set m of regions that affect fault a in the set M;

[0153] Obtain all region combinations in the verification record that are judged to be fault a and whose set contains M. The total number of regions in all region combinations is J;

[0154] Calculate the characteristic influencing factors of the fault area of fault a on the fault diagnosis process of the deep learning model:

[0155] ;

[0156] Where γ represents the correction coefficient;

[0157] Evaluation of the impact index of regional combinations on the fault diagnosis of deep learning models:

[0158] ;

[0159] in, Respectively represent the 1st, 2nd, 3rd, ..., ith regions in the region combination of fault a, and i>2

[0160] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A high-voltage switchgear fault diagnosis method based on deep learning, characterized in that: The following steps are involved: The switchgear is divided into several areas based on its operating functions, and the operating data of several areas are collected based on the partitioning results; Among them, the operating data includes independent variable data and dependent variable data; The operating data of each area is divided based on the public operation conditions of each area; Build a switchgear simulation model, input the operating data of each area of the switchgear into the simulation model for simulation training, obtain simulation result data, and obtain switchgear fault information by comparing the simulation result data with the operating data; Building a data set based on the fault information, building a neural network model, and training the deep learning model through the fault information to obtain a fault diagnosis model; The process of constructing a data set based on the fault information, building a deep learning model, and training the deep learning model using the fault information includes: Extracting characteristic data from fault data to obtain a characteristic data set; Divide the feature dataset into training set and validation set; Build a deep learning model and input the training set into the deep learning model for training; The trained model is validated using the validation set. Based on the distribution of the number of switchgear areas identified as faulty in the validation records, all validation records for each area are categorized. Regional combinations that can influence fault diagnosis are extracted, and the impact index of these regional combinations on the deep learning model's fault diagnosis is evaluated. Optimize the deep learning model based on the distribution of regions in the regional combination where the fault diagnosis impact index is greater than the preset threshold; Input the operating data of the switchgear into the fault diagnosis model to obtain the fault diagnosis results of the target switchgear; The step of evaluating the impact index of the regional combination on the fault diagnosis of the deep learning model specifically includes: Obtain verification records where only a certain area is a fault area and determine it as fault a; Obtain a region combination that has been determined to be fault a at least once in the verification record, where the region combination set is M and the total number of regions in the region combination is I, and determine a set m in the set M that affects fault a; Obtain all region combinations that are determined to be fault a and include set M in the verification record. The total number of regions in all region combinations is J. Calculate the characteristic influencing factors of the fault area of fault a on the fault diagnosis process of the deep learning model: ； Where γ represents the correction coefficient; Evaluation of the impact index of regional combinations on the fault diagnosis of deep learning models: ； in, They represent the 1st, 2nd, 3rd, ..., ith areas in the area combination of fault a, and i>

2.

2. A high-voltage switchgear fault diagnosis method based on deep learning according to claim 1, characterized in that: The method for establishing the switch cabinet simulation model includes: Establish a physical model of the switchgear to ensure that the switchgear model parameters are consistent with the switchgear reagent properties; Set the switch cabinet model environmental parameters according to the switch cabinet working environment; After setting all parameters, simulation calculations are performed to calculate the operating parameters and operating status of the switchgear. After the calculations are completed, the simulation results are analyzed and evaluated.

3. A high-voltage switchgear fault diagnosis method based on deep learning according to claim 2, characterized in that: Comparing the simulation result data and the operation data to obtain the switch cabinet fault information includes: Arrange and identify the simulation result data and the independent variable data in the operation data of each area in time sequence and correspond them; Extract key parameters from simulation result data and regional operation data; Compare the key parameters of the independent variable data corresponding to the dependent variable data in the regional operation data with the key parameters of the corresponding simulation result data; Determine the fault node, analyze the key parameter characteristics of other areas based on the timing characteristics of the fault node, determine the fault type corresponding to the fault node, and obtain fault information.

4. A high-voltage switchgear fault diagnosis system based on deep learning, used to implement the high-voltage switchgear fault diagnosis method based on deep learning according to any one of claims 1 to 3, characterized in that: include: The area division module divides the switchgear into several areas based on its operating functions and collects the operating data of several areas based on the partitioning results; Among them, the operating data includes independent variable data and dependent variable data; The region division module is further used to divide the operation data of each region based on the operation conditions of each region; A model building module is used to build a switchgear simulation model, input the operating data of each area of the switchgear into the simulation model for simulation training, obtain simulation result data, and obtain switchgear fault information by comparing the simulation result data with the operating data; it is also used to build a data set based on the fault information, build a neural network model, and train the deep learning model with the fault information to obtain a fault diagnosis model; A fault diagnosis module is used to input the operating data of the switchgear into the fault diagnosis model to obtain the fault diagnosis results of the target switchgear; The process of constructing a data set based on the fault information, building a deep learning model, and training the deep learning model using the fault information includes: Extracting characteristic data from fault data to obtain a characteristic data set; Divide the feature dataset into training set and validation set; Build a deep learning model and input the training set into the deep learning model for training; The trained model is validated using the validation set. Based on the distribution of the number of switchgear areas identified as faulty in the validation records, all validation records for each area are categorized. Regional combinations that can influence fault diagnosis are extracted, and the impact index of these regional combinations on the deep learning model's fault diagnosis is evaluated. Optimize the deep learning model based on the distribution of regions in the regional combination where the fault diagnosis impact index is greater than the preset threshold; The step of evaluating the impact index of the regional combination on the fault diagnosis of the deep learning model specifically includes: Obtain verification records where only a certain area is a fault area and determine it as fault a; Obtain a region combination that has been determined to be fault a at least once in the verification record, where the region combination set is M and the total number of regions in the region combination is I, and determine a set m in the set M that affects fault a; Obtain all region combinations that are determined to be fault a and include set M in the verification record. The total number of regions in all region combinations is J. Calculate the characteristic influencing factors of the fault area of fault a on the fault diagnosis process of the deep learning model: ； Where γ represents the correction coefficient; Evaluation of the impact index of regional combinations on the fault diagnosis of deep learning models: ； in, They represent the 1st, 2nd, 3rd, ..., ith areas in the area combination of fault a, and i>

2.

5. A high-voltage switchgear fault diagnosis system based on deep learning according to claim 4, characterized in that: The method for establishing the switch cabinet simulation model includes: Establish a physical model of the switchgear to ensure that the switchgear model parameters are consistent with the switchgear reagent properties; Set the switch cabinet model environmental parameters according to the switch cabinet working environment; After setting all parameters, simulation calculations are performed to calculate the operating parameters and operating status of the switchgear. After the calculations are completed, the simulation results are analyzed and evaluated.

6. A high-voltage switchgear fault diagnosis system based on deep learning according to claim 5, characterized in that: Comparing the simulation result data and the operation data to obtain the switch cabinet fault information includes: Arrange and identify the simulation result data and the independent variable data in the operation data of each area in time sequence and correspond them; Extract key parameters from simulation result data and regional operation data; Compare the key parameters of the independent variable data corresponding to the dependent variable data in the regional operation data with the key parameters of the corresponding simulation result data; Determine the fault node, analyze the key parameter characteristics of other areas based on the timing characteristics of the fault node, determine the fault type corresponding to the fault node, and obtain fault information.

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