A partial discharge pattern recognition method, system, terminal device and storage medium for GIS equipment

Abstract

The present invention discloses a method, system, terminal device and storage medium for identifying partial discharge patterns of GIS equipment. The method constructs a multi-dimensional partial discharge feature map based on discharge data, time domain signals and frequency domain signals of the GIS equipment to be identified, providing comprehensive data support for subsequent partial discharge pattern recognition operations performed by a partial discharge pattern recognition model on the GIS equipment to be identified. The partial discharge pattern recognition model is then able to comprehensively and fully perform partial discharge pattern recognition on the GIS equipment to be identified, thereby improving the accuracy of the partial discharge pattern recognition results. This solves the problem that the prior art uses a single signal to generate a feature distribution map, resulting in the feature distribution map only reflecting partial features of the partial discharge, and thus resulting in a low accuracy of the partial discharge pattern recognition results obtained by performing partial discharge pattern recognition on the feature distribution map by a neural network model.

Description

Technical Field

[0001] The present invention relates to the field of partial discharge pattern recognition, and in particular to a partial discharge pattern recognition method, system, terminal equipment and storage medium for GIS equipment. Background Art

[0002] With the rapid development of grid interconnection, the research and application of renewable energy generation technologies, low-loss, long-distance, reliable transmission equipment, and intelligent grids have become a focus of the world's major power producers. Traditional high-voltage AC transmission technology, due to its high line losses, instability, and high line costs, is unable to adapt to the demands of modern, large-capacity grid interconnection, significantly limiting grid construction and development. This has led to the development of high-voltage direct current (HVDC). In the actual high-voltage, long-distance transmission of electricity, research on HVDC GIS equipment is also gaining momentum to enhance the flexibility of HVDC transmission line corridor selection and ensure reliable transmission in harsh environments.

[0003] Partial discharge (PD) is a discharge that occurs between electrodes but does not penetrate them. It occurs due to internal insulation weaknesses or defects during the manufacturing process, leading to repeated breakdown and extinction under high electric field strength. PD is a major cause of primary insulation failure in electrical equipment and a key macroscopic indicator of insulation degradation.

[0004] Currently, the technology for identifying partial discharge patterns in GIS equipment typically uses time-domain or frequency-domain partial discharge signals to generate a characteristic distribution map. This map is then used to identify the partial discharge pattern using a neural network model. However, because the characteristic distribution map generated by this method is based on a single signal, it only reflects a subset of the partial discharge characteristics. Consequently, the accuracy of the partial discharge pattern recognition results obtained by the neural network model using the characteristic distribution map is low. Summary of the Invention

[0005] The present invention provides a method, system, terminal device and storage medium for local discharge pattern recognition of GIS equipment, which can solve the problem that the existing technology uses a single signal to generate a characteristic distribution map, resulting in the characteristic distribution map only reflecting part of the characteristics of the local discharge, and further leading to the low accuracy of the local discharge pattern recognition results obtained by the neural network model performing local discharge pattern recognition on the characteristic distribution map.

[0006] In order to solve the above technical problems, an embodiment of the present invention provides a method for identifying partial discharge patterns of GIS equipment, comprising:

[0007] Obtaining discharge data, time domain signals, and frequency domain signals of partial discharge of the GIS equipment to be identified; wherein the discharge data includes discharge frequency, discharge time interval, and discharge amount;

[0008] Perform time domain feature processing on the time domain signal to obtain the time domain envelope features of the GIS device to be identified;

[0009] According to the discharge frequency and the time domain envelope characteristics, several significant characteristic parameters are determined, and based on the several significant characteristic parameters, a scatter distribution characteristic diagram of the significant characteristic parameters is constructed;

[0010] Based on the discharge frequency, discharge time interval, and discharge amount, a two-dimensional discharge distribution characteristic map of the partial discharge of the GIS equipment to be identified is constructed, and the discharge density of the partial discharge of the GIS equipment to be identified is calculated. Based on the two-dimensional discharge distribution characteristic map and the discharge density, a discharge density map is constructed.

[0011] The frequency characteristics of the frequency domain signal are extracted to obtain the discharge frequency characteristics of each power generation frequency. Based on the discharge frequency characteristics of each power generation frequency, a two-dimensional frequency distribution map of the GIS equipment to be identified is constructed.

[0012] The significant characteristic parameter scatter point distribution characteristic map, discharge density map and two-dimensional frequency distribution map are input into the trained partial discharge recognition model, so that the trained partial discharge recognition model performs partial discharge pattern recognition based on the significant characteristic parameter scatter point distribution characteristic map, discharge density map and two-dimensional frequency distribution map, and obtains the partial discharge pattern recognition result of the GIS equipment to be identified.

[0013] Furthermore, several significant characteristic parameters are determined based on the discharge frequency and time domain envelope characteristics, including:

[0014] According to the discharge frequency and time domain envelope characteristics, the characteristic parameter matrix of the partial discharge signal is constructed, and the information content and information weight of each characteristic parameter are calculated based on the characteristic parameter matrix;

[0015] According to the information content and information weight of each characteristic parameter, several significant characteristic parameters are determined.

[0016] Furthermore, the calculation of the information content and information weight of each characteristic parameter according to the characteristic parameter matrix includes:

[0017] According to the characteristic parameter matrix, the degree of aggregation of the same category and the degree of dispersion between categories of each characteristic parameter are determined, and the amount of information of each characteristic parameter is calculated according to the degree of aggregation of the same category and the degree of dispersion between categories of each characteristic parameter;

[0018] According to the information content of each feature parameter, the information weight of each feature parameter is calculated.

[0019] Furthermore, the information amount calculation formula and the information amount weight calculation formula are as follows:

[0020] The information volume calculation formula is:

[0021] ;

[0022] in, Represents the amount of information of the kth feature parameter; Indicates the degree of clustering of the kth characteristic parameter; Represents the inter-class dispersion of the k-th feature parameter;

[0023] The information weight calculation formula is:

[0024] ;

[0025] in, represents the information weight of the kth feature parameter; K represents the total number of feature parameters; Represents the amount of information of the kth feature parameter; Represents the total amount of information of feature parameters.

[0026] Furthermore, the two-dimensional discharge distribution characteristic map of the partial discharge of the GIS equipment to be identified is constructed based on the discharge frequency, discharge time interval and discharge amount, including:

[0027] According to the discharge frequency, discharge time interval and discharge amount, the maximum discharge amount and maximum discharge time interval of each discharge frequency are determined, and the discharge amount interval and discharge time interval interval are constructed;

[0028] Divide the discharge amount interval and the discharge time interval into a plurality of subintervals, and calculate the mean discharge parameter of the previous and next discharge frequencies in each subinterval; wherein the discharge parameter mean includes the discharge amount mean and the discharge time interval mean;

[0029] According to the mean values ​​of the discharge parameters of the previous and next discharges in each subinterval of each discharge frequency, a two-dimensional discharge distribution characteristic map of the partial discharge of the GIS equipment to be identified is constructed.

[0030] Furthermore, the calculation formula for calculating the mean value of the discharge parameters of the previous and next discharge frequencies in each subinterval is as follows:

[0031] The calculation formula of the previous discharge amount average is:

[0032] ;

[0033] in, Indicates the average value of the previous discharge; Indicates in The total number of subintervals; Indicates the kth frequency in the The discharge amount before the sub-interval;

[0034] The calculation formula of the last discharge amount mean value is:

[0035] ;

[0036] in, Indicates the average value of the last discharge; Indicates in The total number of subintervals; Indicates the kth frequency in the The amount of discharge after each sub-interval;

[0037] The calculation formula of the mean value of the previous discharge time interval is:

[0038]

[0039] in, represents the mean time interval of the previous discharge; Indicates in The total number of subintervals; Indicates the kth frequency in the The time interval between the last discharge of the subinterval;

[0040] The calculation formula of the mean value of the last discharge time interval is:

[0041] ;

[0042] in, represents the mean time interval of the last discharge; Indicates in The total number of subintervals; Indicates the kth frequency in the The time interval between discharges after a subinterval.

[0043] Furthermore, the partial discharge recognition model includes an initial recognition module, a BPA calculation module and a final recognition module; the initial recognition module includes a convolutional neural network unit;

[0044] The trained partial discharge recognition model performs partial discharge pattern recognition based on a significant characteristic parameter scatter distribution feature map, a discharge density map, and a two-dimensional frequency distribution map, including:

[0045] The convolutional neural network unit of the initial recognition module extracts image features and recognizes discharge patterns from the scatter distribution feature map of significant characteristic parameters, the discharge density map, and the two-dimensional frequency distribution map, thereby obtaining initial recognition results and recognition accuracy of partial discharge patterns in different dimensions; wherein the different dimensions include the time domain dimension, the frequency domain dimension, and the time-frequency dimension;

[0046] According to the recognition accuracy of partial discharge patterns in different dimensions, the basic probability value of recognition in each dimension is calculated through the BPA calculation module;

[0047] According to the basic probability value of identification in each dimension, the fusion value of the basic probability value of identification in any two dimensions is calculated through the final recognition module, and the initial recognition results of the partial discharge patterns in the two dimensions with the maximum fusion value are fused to obtain the partial discharge pattern recognition result of the GIS equipment to be identified.

[0048] Based on the above method embodiment, the present invention provides a corresponding system embodiment;

[0049] An embodiment of the present invention provides a partial discharge pattern recognition system for GIS equipment, comprising: a data acquisition module, a first feature map generation module, a second feature map generation module, a third feature map generation module, and a partial discharge pattern recognition module;

[0050] The data acquisition module is used to acquire discharge data, time domain signals and frequency domain signals of the local discharge of the GIS equipment to be identified; wherein the discharge data includes discharge frequency, discharge time interval and discharge amount;

[0051] The first feature map generation module is used to perform time domain feature processing on the time domain signal to obtain the time domain envelope feature of the GIS device to be identified, determine a number of significant feature parameters based on the discharge frequency and the time domain envelope feature, and construct a scatter distribution feature map of the significant feature parameters based on the several significant feature parameters;

[0052] The second characteristic map generation module is used to construct a two-dimensional discharge distribution characteristic map of the local discharge of the GIS equipment to be identified based on the discharge frequency, discharge time interval and discharge amount, calculate the discharge density of the local discharge of the GIS equipment to be identified, and construct a discharge density map based on the two-dimensional discharge distribution characteristic map and the discharge density;

[0053] The third feature map generation module is used to extract frequency features from the frequency domain signal to obtain the discharge frequency domain features of each power generation frequency, and construct a two-dimensional frequency distribution map of the GIS device to be identified based on the discharge frequency domain features of each power generation frequency;

[0054] The partial discharge pattern recognition module is used to input the significant characteristic parameter scatter point distribution characteristic map, the discharge density map and the two-dimensional frequency distribution map into the trained partial discharge recognition model, so that the trained partial discharge recognition model performs partial discharge pattern recognition based on the significant characteristic parameter scatter point distribution characteristic map, the discharge density map and the two-dimensional frequency distribution map, and obtains the partial discharge pattern recognition result of the GIS device to be identified.

[0055] Based on the above-mentioned method embodiment, the present invention provides a corresponding terminal device embodiment, including: a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements a local discharge pattern recognition method for GIS equipment as described in the present invention.

[0056] Based on the above-mentioned method embodiment, the present invention provides a corresponding computer-readable storage medium embodiment, including: a stored computer program, which, when the computer program is running, controls the device where the computer-readable storage medium is located to execute a local discharge pattern recognition method for GIS equipment as described in the present invention.

[0057] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0058] The present invention constructs a scatter distribution characteristic diagram of significant characteristic parameters of the partial discharge of the DC GIS device to be identified, a discharge density map and a two-dimensional frequency distribution map based on the discharge data, time domain signals and frequency domain signals of the partial discharge of the DC GIS device to be identified, and uses the constructed scatter distribution characteristic diagram of significant characteristic parameters, the discharge density map and the two-dimensional frequency distribution map as input data of a partial discharge pattern recognition model, so that the partial discharge pattern recognition model can perform partial discharge pattern recognition on the DC GIS device to be identified. That is, the present invention constructs a multi-dimensional partial discharge characteristic diagram based on the discharge data, the time domain signal and the frequency domain signal, and provides comprehensive data support for the subsequent partial discharge pattern recognition operation of the partial discharge pattern recognition model on the GIS device to be identified, so that the partial discharge pattern recognition model can comprehensively and fully perform partial discharge pattern recognition on the GIS device to be identified, thereby improving the accuracy of the partial discharge pattern recognition result, and solving the problem that the prior art uses a single signal to generate a characteristic distribution map, which results in the characteristic distribution map only reflecting part of the characteristics of the partial discharge, and thus leads to low accuracy of the partial discharge pattern recognition result obtained by the neural network model performing partial discharge pattern recognition on the characteristic distribution map. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 : A flowchart of a method for identifying partial discharge patterns of GIS equipment provided by an embodiment of the present invention;

[0060] Figure 2 : A system structure diagram of a partial discharge pattern recognition system for GIS equipment provided by an embodiment of the present invention;

[0061] Figure 3 : An example diagram of the scatter distribution characteristics of significant characteristic parameters constructed experimentally according to an embodiment of the present invention;

[0062] Figure 4: Example graph of discharge density constructed experimentally according to an embodiment of the present invention;

[0063] Figure 5 : An example graph of a two-dimensional frequency distribution constructed experimentally according to an embodiment of the present invention; DETAILED DESCRIPTION

[0064] The following will clearly and completely describe 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 the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0065] In the description of the present invention, it should be understood that the terms "first", "second" and "third" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features.

[0066] Example 1:

[0067] Reference Figure 1 , is a flowchart of a method for identifying partial discharge patterns of GIS equipment provided by an embodiment of the present invention, the method comprising at least the following steps:

[0068] Step S1: obtaining discharge data, time domain signals, and frequency domain signals of partial discharge of the GIS equipment to be identified; wherein the discharge data includes discharge frequency, discharge time interval, and discharge amount;

[0069] In this embodiment, the discharge data of the partial discharge signal of the GIS device to be identified can be collected and acquired using, but not limited to, the MPD800 partial discharge measuring instrument, and the time domain signal and frequency domain signal of the partial discharge of the GIS device to be identified can be acquired using, but not limited to, the ultra-high frequency antenna.

[0070] Step S2: Perform time domain feature processing on the time domain signal to obtain the time domain envelope feature of the GIS device to be identified;

[0071] In this embodiment, the time domain feature processing is performed on the time domain signal through Hilbert transform-average filtering to obtain the time domain envelope feature of the GIS device to be identified.

[0072] Step S3: determining a number of significant characteristic parameters based on the discharge frequency and the time domain envelope characteristics, and constructing a scatter distribution characteristic diagram of the significant characteristic parameters based on the number of significant characteristic parameters;

[0073] In this embodiment, several significant characteristic parameters are determined based on the discharge frequency and the time domain envelope characteristics, including:

[0074] According to the discharge frequency and time domain envelope characteristics, the characteristic parameter matrix of the partial discharge signal is constructed, and the information content and information weight of each characteristic parameter are calculated based on the characteristic parameter matrix;

[0075] In this embodiment, constructing the characteristic parameter matrix of the partial discharge signal further includes: performing normalization processing on the constructed characteristic parameter matrix of the partial discharge signal;

[0076] According to the information content and information weight of each characteristic parameter, several significant characteristic parameters are determined;

[0077] In this embodiment, the significant feature parameters include but are not limited to mean, variance, skewness, and steepness.

[0078] In this embodiment, the calculation of the information content and information weight of each characteristic parameter according to the characteristic parameter matrix includes:

[0079] According to the characteristic parameter matrix, the degree of aggregation of the same category and the degree of dispersion between categories of each characteristic parameter are determined, and the amount of information of each characteristic parameter is calculated according to the degree of aggregation of the same category and the degree of dispersion between categories of each characteristic parameter;

[0080] In this embodiment, the information amount calculation formula is:

[0081] ;

[0082] in, Represents the amount of information of the kth feature parameter; Indicates the degree of clustering of the kth characteristic parameter; Represents the inter-class dispersion of the k-th feature parameter; The smaller the value, the stronger the ability of the characteristic parameter to separate different types of partial discharge signals;

[0083] According to the information amount of each characteristic parameter, the information amount weight of each characteristic parameter is calculated;

[0084] In this embodiment, the information weight calculation formula is:

[0085] ;

[0086] in, represents the information weight of the kth feature parameter; K represents the total number of feature parameters; Represents the amount of information of the kth feature parameter; Represents the sum of the information content of the characteristic parameters; The smaller the value, the stronger the ability of the characteristic parameter to separate different discharge signals and the higher the degree of aggregation of similar discharge signals.

[0087] Step S4: constructing a two-dimensional discharge distribution characteristic map of the partial discharge of the GIS equipment to be identified based on the discharge frequency, discharge time interval, and discharge amount, and calculating the discharge density of the partial discharge of the GIS equipment to be identified. Based on the two-dimensional discharge distribution characteristic map and the discharge density, a discharge density map is constructed;

[0088] In this embodiment, the two-dimensional discharge distribution characteristic map of the partial discharge of the GIS equipment to be identified is constructed based on the discharge frequency, discharge time interval and discharge amount, including:

[0089] According to the discharge frequency, discharge time interval and discharge amount, the maximum discharge amount and maximum discharge time interval of each discharge frequency are determined, and the discharge amount interval and discharge time interval interval are constructed;

[0090] Divide the discharge amount interval and the discharge time interval into a plurality of subintervals, and calculate the mean discharge parameter of the previous and next discharge frequencies in each subinterval; wherein the discharge parameter mean includes the discharge amount mean and the discharge time interval mean;

[0091] According to the mean values ​​of the discharge parameters of the previous and next discharges in each subinterval of each discharge frequency, a two-dimensional discharge distribution characteristic map of the partial discharge of the GIS equipment to be identified is constructed.

[0092] In this embodiment, the calculation formula of the discharge density is: discharge density=total discharge frequency / discharge time; wherein the total discharge frequency and discharge time are both the total discharge frequency and discharge time in each sub-interval.

[0093] In this embodiment, the calculation formula for calculating the mean value of the discharge parameters of the previous and next discharges in each subinterval of each discharge frequency is as follows:

[0094] The calculation formula of the previous discharge amount average is:

[0095] ;

[0096] in, Indicates the average value of the previous discharge; Indicates in The total number of subintervals; Indicates the kth frequency in the The discharge amount before the sub-interval;

[0097] The calculation formula of the last discharge amount mean value is:

[0098] ;

[0099] in, Indicates the average value of the last discharge; Indicates in The total number of subintervals; Indicates the kth frequency in the The amount of discharge after each sub-interval;

[0100] The calculation formula of the mean value of the previous discharge time interval is:

[0101]

[0102] in, represents the mean time interval of the previous discharge; Indicates in The total number of subintervals; Indicates the kth frequency in the The time interval between the last discharge of the subinterval;

[0103] The calculation formula of the mean value of the last discharge time interval is:

[0104] ;

[0105] in, represents the mean time interval of the last discharge; Indicates in The total number of subintervals; Indicates the kth frequency in the The time interval between discharges after a subinterval.

[0106] Step S5: extracting frequency features from the frequency domain signal to obtain the discharge frequency domain features of each power generation frequency, and constructing a two-dimensional frequency distribution map of the GIS device to be identified based on the discharge frequency domain features of each power generation frequency;

[0107] In this embodiment, the frequency characteristics of the frequency domain signal can be extracted by using, but not limited to, Fourier transform to obtain the discharge frequency domain characteristics of each power generation frequency.

[0108] In this embodiment, the two-dimensional frequency distribution map of the GIS device to be identified is constructed based on the discharge frequency domain characteristics of each power generation frequency, including:

[0109] According to the discharge frequency domain characteristics of each power generation frequency, the interval range of the frequency distribution map is determined, and based on the discharge frequency domain characteristics and interval range, a three-dimensional frequency distribution map is constructed;

[0110] The three-dimensional frequency distribution map is subjected to dimensionality reduction processing to obtain a two-dimensional frequency distribution map of the GIS device to be identified.

[0111] Step S6: Inputting the significant characteristic parameter scatter point distribution characteristic diagram, the discharge density map and the two-dimensional frequency distribution map into the trained partial discharge recognition model, so that the trained partial discharge recognition model performs partial discharge pattern recognition based on the significant characteristic parameter scatter point distribution characteristic diagram, the discharge density map and the two-dimensional frequency distribution map, and obtains the partial discharge pattern recognition result of the GIS device to be identified.

[0112] In this embodiment, referring to Figure 3 、 4 and 5, respectively, are an example graph of the scatter distribution characteristics of significant characteristic parameters, an example graph of discharge density, and an example graph of two-dimensional frequency distribution constructed experimentally according to an embodiment of the present invention; the partial discharge recognition model includes an initial recognition module, a BPA calculation module, and a final recognition module; the initial recognition module includes a convolutional neural network unit;

[0113] The trained partial discharge recognition model performs partial discharge pattern recognition based on a significant characteristic parameter scatter distribution feature map, a discharge density map, and a two-dimensional frequency distribution map, including:

[0114] The convolutional neural network unit of the initial recognition module extracts image features and recognizes discharge patterns from the scatter distribution feature map of significant characteristic parameters, the discharge density map, and the two-dimensional frequency distribution map, thereby obtaining initial recognition results and recognition accuracy of partial discharge patterns in different dimensions; wherein the different dimensions include the time domain dimension, the frequency domain dimension, and the time-frequency dimension;

[0115] According to the recognition accuracy of partial discharge patterns in different dimensions, the basic probability value of recognition in each dimension is calculated through the BPA calculation module;

[0116] According to the basic probability value of identification in each dimension, the fusion value of the basic probability value of identification in any two dimensions is calculated through the final recognition module, and the initial recognition results of the partial discharge patterns in the two dimensions with the maximum fusion value are fused to obtain the partial discharge pattern recognition result of the GIS equipment to be identified.

[0117] In this embodiment, the BPA calculation is specifically as follows:

[0118] Assigning an initial confidence level to each hypothesis: For each hypothesis Hi, an initial confidence level can be assigned to it based on prior knowledge or experience, usually represented by the Bel function, denoted as BelHi;

[0119] For each hypothesis Hi, calculate its uncertainty measures Plausibility and Belief:

[0120] ;

[0121] in, It represents the minimum evidence that can be provided to support the hypothesis that Hi is true;

[0122] ;

[0123] in, represents the probability of the occurrence of evidence Ek; Indicates the degree of confidence in hypothesis Hi given evidence Ej;

[0124] Update the credibility of each hypothesis based on Plausibility and Belief values:

[0125] For each :1. , update the Plausibility value; 2. , update the Belief value; 3. Iterate through the above steps multiple times until convergence;

[0126] Finally get BPA: Finally get the final trust distribution of each hypothesis Hi ; Among them, Φ, Ψ, etc. represent specific events or assumptions.

[0127] In this embodiment, the fusion BPA calculation is specifically as follows:

[0128] For each hypothesis : 1. Calculate joint trust , where Bel_1, Bel_2 and Bel_3 represent the trustworthiness of the three sources respectively; 2. Calculate the normalization factor ; 3. The final joint trust is .

[0129] In this embodiment, the model training of the partial discharge recognition model includes:

[0130] Obtaining historical discharge data, historical time domain signals, and historical frequency domain signals of partial discharge of GIS equipment with defect labels; wherein the historical discharge data includes historical discharge frequency, historical discharge time interval, and historical discharge amount;

[0131] Perform time domain feature processing on historical time domain signals to obtain historical time domain envelope features of GIS equipment with defect labels;

[0132] According to the historical discharge frequency and historical time domain envelope characteristics, several historical significant characteristic parameters of GIS equipment with defect labels are determined, and based on several historical significant characteristic parameters, a scatter distribution characteristic map of historical significant characteristic parameters is constructed;

[0133] Based on the historical discharge frequency, historical discharge time interval, and historical discharge amount, a historical two-dimensional discharge distribution characteristic map of partial discharge of GIS equipment with defect labels is constructed. The historical discharge density of partial discharge of GIS equipment with defect labels is calculated. Based on the historical two-dimensional discharge distribution characteristic map and historical discharge density, a historical discharge density map is constructed.

[0134] Frequency features of historical frequency domain signals are extracted to obtain the historical discharge frequency domain features of each power generation frequency. Based on the historical discharge frequency domain features of each power generation frequency, a historical two-dimensional frequency distribution map of GIS equipment with defect labels is constructed.

[0135] Inputting a historical significant characteristic parameter scatter point distribution characteristic graph, a historical discharge density graph, and a historical two-dimensional frequency distribution graph into a partial discharge recognition model to be trained, so that the partial discharge recognition model to be trained performs partial discharge pattern recognition based on the historical significant characteristic parameter scatter point distribution characteristic graph, the historical discharge density graph, and the historical two-dimensional frequency distribution graph to obtain a partial discharge pattern recognition result; wherein the partial discharge pattern recognition result is a defect result of the GIS equipment; the defect result of the GIS equipment includes but is not limited to a pinpoint defect, a surface contamination defect, a metal particle defect, and an air gap defect;

[0136] The partial discharge pattern recognition results are compared with the actual defect labels to calculate the loss value, and the parameters of the partial discharge recognition model are optimized according to the loss value until the loss value converges to obtain a trained partial discharge recognition model.

[0137] In this embodiment, the loss function for calculating the loss value includes but is not limited to the mean square error function and the mean absolute percentage error function; the method for optimizing the parameters of the future carbon emission prediction model to be trained includes but is not limited to the gradient descent method and the Adam optimizer.

[0138] Example 2:

[0139] Reference Figure 2 , is a system structure diagram of a partial discharge pattern recognition system for GIS equipment provided by an embodiment of the present invention, the system at least comprising: a data acquisition module, a first feature map generation module, a second feature map generation module, a third feature map generation module and a partial discharge pattern recognition module;

[0140] The data acquisition module is used to acquire discharge data, time domain signals and frequency domain signals of the local discharge of the GIS equipment to be identified; wherein the discharge data includes discharge frequency, discharge time interval and discharge amount;

[0141] The first feature map generation module is used to perform time domain feature processing on the time domain signal to obtain the time domain envelope feature of the GIS device to be identified, determine a number of significant feature parameters based on the discharge frequency and the time domain envelope feature, and construct a scatter distribution feature map of the significant feature parameters based on the several significant feature parameters;

[0142] The second characteristic map generation module is used to construct a two-dimensional discharge distribution characteristic map of the local discharge of the GIS equipment to be identified based on the discharge frequency, discharge time interval and discharge amount, calculate the discharge density of the local discharge of the GIS equipment to be identified, and construct a discharge density map based on the two-dimensional discharge distribution characteristic map and the discharge density;

[0143] The third feature map generation module is used to extract frequency features from the frequency domain signal to obtain the discharge frequency domain features of each power generation frequency, and construct a two-dimensional frequency distribution map of the GIS device to be identified based on the discharge frequency domain features of each power generation frequency;

[0144] The partial discharge pattern recognition module is used to input the significant characteristic parameter scatter point distribution characteristic map, the discharge density map and the two-dimensional frequency distribution map into the trained partial discharge recognition model, so that the trained partial discharge recognition model performs partial discharge pattern recognition based on the significant characteristic parameter scatter point distribution characteristic map, the discharge density map and the two-dimensional frequency distribution map, and obtains the partial discharge pattern recognition result of the GIS device to be identified.

[0145] Based on the above method embodiment, another embodiment is provided;

[0146] Another embodiment of the present invention provides a local discharge pattern recognition terminal device for GIS equipment, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the local discharge pattern recognition method for GIS equipment described in any one of the above-mentioned method embodiments of the present invention.

[0147] Exemplarily, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to implement the present invention. The one or more modules / units may be a series of computer program instruction segments capable of performing specific functions, and the instruction segments are used to describe the execution process of the computer program in the partial discharge pattern recognition terminal device of the GIS device.

[0148] The GIS equipment's partial discharge pattern recognition terminal device can be a computing device such as a desktop computer, laptop, PDA, or cloud server. The GIS equipment's partial discharge pattern recognition terminal device can include, but is not limited to, a processor and memory. Those skilled in the art will appreciate that, for example, the GIS equipment's partial discharge pattern recognition terminal device can also include input / output devices, network access devices, buses, and the like.

[0149] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor. The processor serves as the control center of the partial discharge pattern recognition terminal device of the GIS device, and utilizes various interfaces and lines to connect various parts of the partial discharge pattern recognition terminal device of the GIS device.

[0150] The memory can be used to store the computer programs and / or modules. The processor implements the various functions of the partial discharge pattern recognition terminal device for the GIS device by running or executing the computer programs and / or modules stored in the memory and accessing the data stored in the memory. The memory may primarily include a program storage area and a data storage area. The program storage area may store an operating system and at least one application required for a function (such as a sound playback function or an image playback function); the data storage area may store data generated based on the use of the mobile phone (such as audio data and a phone book). Furthermore, the memory may include high-speed random access memory and non-volatile memory, such as a hard disk, internal memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, at least one disk storage device, a flash memory device, or other volatile solid-state storage device.

[0151] Based on the above method embodiment, another embodiment is provided;

[0152] Another embodiment of the present invention provides a storage medium comprising a stored computer program, wherein when the computer program is running, the device where the storage medium is located is controlled to execute a method for identifying a partial discharge pattern of a GIS device as described in any one of the above-mentioned method embodiments of the present invention.

[0153] The aforementioned storage medium is a computer-readable storage medium. If the module / unit integrated into the partial discharge pattern recognition system / terminal device of a GIS device is implemented as a software functional unit and sold or used as a standalone product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention can also implement all or part of the process steps in the above-mentioned method embodiments by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When executed by a processor, the computer program can implement the steps of each of the above-mentioned method embodiments. The computer program includes computer program code, which can be in source code form, object code form, executable file, or some intermediate form. The computer-readable medium can include any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a removable hard drive, a magnetic disk, an optical disk, computer memory, read-only memory (ROM), random access memory (RAM), an electrical carrier signal, a telecommunications signal, and software distribution media.

[0154] It should be noted that the above-mentioned terminal device may include, but is not limited to, a processor and a memory. Those skilled in the art will understand that the above-mentioned terminal device is merely an example and does not constitute a limitation on the terminal device. It may include more or fewer components, or a combination of certain components, or different components.

[0155] The specific embodiments described above further illustrate the objectives, technical solutions, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included within the scope of protection of the present invention for those skilled in the art.

Claims

1. A method for identifying partial discharge patterns of GIS equipment, characterized in that: include: Obtaining discharge data, time domain signals, and frequency domain signals of partial discharge of the GIS equipment to be identified; wherein the discharge data includes discharge frequency, discharge time interval, and discharge amount; Perform time domain feature processing on the time domain signal to obtain the time domain envelope features of the GIS device to be identified; According to the discharge frequency and the time domain envelope characteristics, several significant characteristic parameters are determined, and based on the several significant characteristic parameters, a scatter distribution characteristic diagram of the significant characteristic parameters is constructed; Based on the discharge frequency, discharge time interval, and discharge amount, a two-dimensional discharge distribution characteristic map of the partial discharge of the GIS equipment to be identified is constructed, and the discharge density of the partial discharge of the GIS equipment to be identified is calculated. Based on the two-dimensional discharge distribution characteristic map and the discharge density, a discharge density map is constructed. Frequency characteristics are extracted from the frequency domain signal to obtain the discharge frequency characteristics of each discharge frequency. Based on the discharge frequency characteristics of each discharge frequency, the interval range of the frequency distribution spectrum is determined. Based on the discharge frequency characteristics and interval range, a three-dimensional frequency distribution spectrum is constructed. The three-dimensional frequency distribution spectrum is subjected to dimensionality reduction processing to obtain a two-dimensional frequency distribution spectrum of the GIS device to be identified. The significant characteristic parameter scatter point distribution characteristic map, discharge density map and two-dimensional frequency distribution map are input into the trained partial discharge recognition model, so that the trained partial discharge recognition model performs partial discharge pattern recognition based on the significant characteristic parameter scatter point distribution characteristic map, discharge density map and two-dimensional frequency distribution map, and obtains the partial discharge pattern recognition result of the GIS equipment to be identified.

2. The method for identifying partial discharge patterns of GIS equipment according to claim 1, characterized in that: According to the discharge frequency and time domain envelope characteristics, several significant characteristic parameters are determined, including: According to the discharge frequency and time domain envelope characteristics, the characteristic parameter matrix of the partial discharge signal is constructed, and the information content and information weight of each characteristic parameter are calculated based on the characteristic parameter matrix; According to the information content and information weight of each characteristic parameter, several significant characteristic parameters are determined.

3. The method for identifying partial discharge patterns of GIS equipment according to claim 2, characterized in that: The step of calculating the information content and information weight of each characteristic parameter according to the characteristic parameter matrix includes: According to the characteristic parameter matrix, the degree of aggregation of the same category and the degree of dispersion between categories of each characteristic parameter are determined, and the amount of information of each characteristic parameter is calculated according to the degree of aggregation of the same category and the degree of dispersion between categories of each characteristic parameter; According to the information content of each feature parameter, the information weight of each feature parameter is calculated.

4. The method for identifying partial discharge patterns of GIS equipment according to claim 3, characterized in that: The information amount calculation formula and information amount weight calculation formula are as follows: The information volume calculation formula is: ； in, Represents the amount of information of the kth feature parameter; Indicates the degree of clustering of the kth characteristic parameter; Represents the inter-class dispersion of the k-th feature parameter; The information weight calculation formula is: ； in, represents the information weight of the kth feature parameter; K represents the total number of feature parameters; Represents the amount of information of the kth feature parameter; Represents the total amount of information of feature parameters.

5. The method for identifying partial discharge patterns of GIS equipment according to claim 4, characterized in that: The method of constructing a two-dimensional discharge distribution characteristic map of the local discharge of the GIS equipment to be identified based on the discharge frequency, discharge time interval and discharge amount includes: According to the discharge frequency, discharge time interval and discharge amount, the maximum discharge amount and maximum discharge time interval of each discharge frequency are determined, and the discharge amount interval and discharge time interval interval are constructed; Divide the discharge amount interval and the discharge time interval into a plurality of subintervals, and calculate the mean discharge parameter of the previous and next discharge frequencies in each subinterval; wherein the discharge parameter mean includes the discharge amount mean and the discharge time interval mean; According to the mean values ​​of the discharge parameters of the previous and next discharges in each subinterval of each discharge frequency, a two-dimensional discharge distribution characteristic map of the partial discharge of the GIS equipment to be identified is constructed.

6. The method for identifying partial discharge patterns of GIS equipment according to claim 5, characterized in that: The calculation formula for calculating the mean value of the discharge parameters of the previous and next discharge frequencies in each subinterval is as follows: The calculation formula for the previous discharge average is: ； in, Indicates the average value of the previous discharge; Indicates in The total number of subintervals; Indicates the kth frequency in the The discharge amount before the sub-interval; The calculation formula for the average value of the last discharge is: ； in, Indicates the average value of the last discharge; Indicates in The total number of subintervals; Indicates the kth frequency in the The amount of discharge after each sub-interval; The calculation formula for the mean time interval of the previous discharge is: in, represents the mean time interval of the previous discharge; Indicates in The total number of subintervals; Indicates the kth frequency in the The time interval between the last discharge of each subinterval; The calculation formula for the mean time interval of the last discharge is: ； in, represents the mean time interval of the last discharge; Indicates in The total number of subintervals; Indicates the kth frequency in the The time interval between discharges after a subinterval.

7. The method for identifying partial discharge patterns of GIS equipment according to claim 6, characterized in that: The partial discharge recognition model includes an initial recognition module, a BPA calculation module and a final recognition module; the initial recognition module includes a convolutional neural network unit; The trained partial discharge recognition model performs partial discharge pattern recognition based on a significant characteristic parameter scatter distribution feature map, a discharge density map, and a two-dimensional frequency distribution map, including: The convolutional neural network unit of the initial recognition module extracts image features and recognizes discharge patterns from the scatter distribution feature map of significant characteristic parameters, the discharge density map, and the two-dimensional frequency distribution map, thereby obtaining initial recognition results and recognition accuracy of partial discharge patterns in different dimensions; wherein the different dimensions include the time domain dimension, the frequency domain dimension, and the time-frequency dimension; According to the recognition accuracy of partial discharge patterns in different dimensions, the basic probability value of recognition in each dimension is calculated through the BPA calculation module; According to the basic probability value of identification in each dimension, the fusion value of the basic probability value of identification in any two dimensions is calculated through the final recognition module, and the initial recognition results of the partial discharge patterns in the two dimensions with the maximum fusion value are fused to obtain the partial discharge pattern recognition result of the GIS equipment to be identified.

8. A partial discharge pattern recognition system for GIS equipment, characterized in that: include: A data acquisition module, a first characteristic map generation module, a second characteristic map generation module, a third characteristic map generation module and a partial discharge pattern recognition module; The data acquisition module is used to acquire discharge data, time domain signals and frequency domain signals of the local discharge of the GIS equipment to be identified; wherein the discharge data includes discharge frequency, discharge time interval and discharge amount; The first feature map generation module is used to perform time domain feature processing on the time domain signal to obtain the time domain envelope feature of the GIS device to be identified, determine a number of significant feature parameters based on the discharge frequency and the time domain envelope feature, and construct a scatter distribution feature map of the significant feature parameters based on the several significant feature parameters; The second characteristic map generation module is used to construct a two-dimensional discharge distribution characteristic map of the local discharge of the GIS equipment to be identified based on the discharge frequency, discharge time interval and discharge amount, calculate the discharge density of the local discharge of the GIS equipment to be identified, and construct a discharge density map based on the two-dimensional discharge distribution characteristic map and the discharge density; The third feature map generation module is used to extract frequency features from the frequency domain signal to obtain the discharge frequency domain features of each discharge frequency, determine the interval range of the frequency distribution map based on the discharge frequency domain features of each discharge frequency, and construct a three-dimensional frequency distribution map based on the discharge frequency domain features and the interval range; perform dimensionality reduction processing on the three-dimensional frequency distribution map to obtain a two-dimensional frequency distribution map of the GIS device to be identified; The partial discharge pattern recognition module is used to input the significant characteristic parameter scatter point distribution characteristic map, the discharge density map and the two-dimensional frequency distribution map into the trained partial discharge recognition model, so that the trained partial discharge recognition model performs partial discharge pattern recognition based on the significant characteristic parameter scatter point distribution characteristic map, the discharge density map and the two-dimensional frequency distribution map, and obtains the partial discharge pattern recognition result of the GIS device to be identified.

9. A local discharge pattern recognition terminal device for GIS equipment, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, it implements a local discharge pattern recognition method for GIS equipment as described in any one of claims 1 to 7.

10. A storage medium, characterized in that: The storage medium includes a stored computer program, wherein when the computer program is running, the device where the storage medium is located is controlled to execute the partial discharge pattern recognition method for GIS equipment according to any one of claims 1 to 7.

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