GIS Equipment Discharge Type Determination Method, Device, Electronic Equipment and Storage Medium

Abstract

The present invention relates to the technical field of abnormal diagnosis of GIS equipment, and particularly relates to a method, device, electronic device and storage medium for determining the discharge type of GIS equipment. The method of the present invention first obtains a discharge noise waveform; then extracts time-frequency domain features from the discharge noise waveform to obtain a time-frequency domain matrix; then extracts a plurality of frequency domain vectors from the time-frequency domain matrix, and constructs a plurality of discharge type queues according to a standard frequency domain vector set and the plurality of frequency domain vectors; finally, according to the plurality of discharge type queues, determines the probabilities of a plurality of discharge types, and determines the discharge type of the discharge noise waveform according to the probabilities of the plurality of discharge types. By extracting the time-frequency domain matrix of the discharge noise waveform, the present invention extracts frequency domain vectors from the time-frequency domain matrix for similarity comparison with the standard frequency domain vector set, and determines the discharge type according to the comparison result, and the analysis result of the discharge type is more accurate and reliable.

Description

Technical Field

[0001] The present invention relates to the technical field of abnormal diagnosis of GIS equipment, and particularly relates to a method, device, electronic device and storage medium for determining the discharge type of GIS equipment. Background Art

[0002] Gas Insulated Switch (GIS) is composed of a circuit breaker, disconnector, earthing switch, current transformer, voltage transformer, lightning arrester, busbar, connecting piece and outgoing terminal, etc. These devices or components are all enclosed in a metal earthed shell, and an insulating gas (usually SF6) with a certain pressure is filled inside, so it is also called an all - enclosed gas - insulated combined electrical apparatus.

[0003] GIS is a high - voltage electrical equipment with high operation reliability, less maintenance workload and long overhaul period. Its failure rate is only 20% - 40% of that of conventional equipment. However, GIS also has its inherent disadvantages. Due to factors such as SF6 gas leakage, external moisture infiltration, existence of conductive impurities, and insulator aging, internal flashover faults of GIS may occur. The fully - enclosed structure of GIS makes it difficult to locate and repair faults. The repair work is complicated, and the average power outage repair time after an accident is longer than that of conventional equipment. Its power outage range is large and often involves many non - fault components.

[0004] In some related technologies, an ultrasonic detection device is installed on the GIS equipment, which can extract the noise waveform generated during discharge. Through the noise waveform, the time and position of discharge can be roughly inferred. In some technologies, the type of discharge is determined by using artificial intelligence algorithms after processing the noise waveform. These artificial intelligence algorithms usually require a large number of samples to achieve better analysis results. However, limited by the possibility of discharge occurrence, it is not easy to obtain a large number of samples. Therefore, the analysis results of the discharge type of GIS equipment in the prior art are not accurate.

[0005] Based on this, it is necessary to develop and design a method for determining the discharge type of GIS equipment. Summary of the Invention

[0006] Embodiments of the present invention provide a method, device, electronic device and storage medium for determining the discharge type of GIS equipment, which are used to solve the problem that the analysis results of the discharge type of GIS equipment in the prior art are not accurate.

[0007] In a first aspect, an embodiment of the present invention provides a method for determining the discharge type of GIS equipment, including:

[0008] Obtain a discharge noise waveform;

[0009] Extract time - frequency domain features from the discharge noise waveform to obtain a time - frequency domain matrix;

[0010] Extract a plurality of frequency-domain vectors from the time-frequency domain matrix, and construct a plurality of discharge type queues according to the standard frequency-domain vector set and the plurality of frequency-domain vectors, wherein the standard frequency-domain vector set represents the discharge types corresponding to the plurality of standard frequency-domain vectors;

[0011] Determine the probabilities of a plurality of discharge types according to the plurality of discharge type queues, and determine the discharge type of the discharge noise waveform according to the probabilities of the plurality of discharge types.

[0012] In a possible implementation manner, the extracting time-frequency domain features from the discharge noise waveform to obtain a time-frequency domain matrix includes:

[0013] Discretize the discharge noise waveform to obtain a discharge data set;

[0014] Obtain a plurality of characteristic frequencies;

[0015] Successively take out the characteristic frequencies from the plurality of characteristic frequencies as the frequencies to be processed;

[0016] Successively take out a plurality of data segments from the discharge data set in a sliding manner;

[0017] Perform frequency-domain feature extraction on the plurality of data segments according to the frequency to be processed to obtain a plurality of frequency-domain features, wherein each frequency-domain feature corresponds to one data segment;

[0018] Construct the plurality of frequency-domain features into a frequency-domain feature vector according to the positions of the plurality of data segments in the discharge data set;

[0019] Add the frequency-domain feature vector of the feature vector to the time-frequency domain matrix;

[0020] If the traversal of the plurality of characteristic frequencies is not completed, jump to the step of successively taking out the characteristic frequencies from the plurality of characteristic frequencies as the frequencies to be processed.

[0021] In a possible implementation manner, the performing frequency-domain feature extraction on the plurality of data segments according to the frequency to be processed to obtain a plurality of frequency-domain features includes:

[0022] Perform frequency-domain feature extraction on the plurality of data segments according to the first formula and the frequency to be processed to obtain a plurality of frequency-domain features, wherein the first formula is:

[0023]

[0024] wherein, DF(n) is the nth frequency-domain feature, Dnmax is the total number of data in the first data segment, DS n(Dn) is the (Dn + 1)-th data of the n-th data segment, e is the natural constant, j is the imaginary unit, ω is the frequency to be processed, Tn is the number of data within the data segment during the period corresponding to the frequency to be processed, and π is the circumference ratio.

[0025] In a possible implementation manner, a plurality of frequency-domain vectors are extracted from the time-frequency domain matrix, and a plurality of discharge type queues are constructed according to the standard frequency-domain vector set and the plurality of frequency-domain vectors, where the standard frequency-domain vector set represents the discharge types corresponding to the plurality of standard frequency-domain vectors, including:

[0026] Obtain a plurality of standard frequency-domain vector sets, where each standard frequency-domain vector set corresponds to a frequency-domain frequency, the standard frequency-domain vector set includes a first weight vector and a plurality of standard frequency-domain vectors, and each standard frequency-domain vector corresponds to a discharge type;

[0027] Traversingly extract a frequency-domain vector from the time-frequency domain matrix as the frequency-domain vector to be processed, and perform the following steps after each extraction:

[0028] According to the frequency-domain frequency of the frequency-domain vector to be processed, extract the target standard frequency-domain vector set from the plurality of standard frequency-domain vector sets;

[0029] Perform weight processing on the frequency-domain vector to be processed by using the target weight vector to obtain an intermediate frequency-domain vector, where the target weight vector is the first weight vector in the target standard frequency-domain vector set;

[0030] According to the second formula, determine the similarity coefficient between the intermediate frequency-domain vector and each candidate standard frequency-domain vector, so as to obtain a plurality of similarity coefficients, where the candidate standard frequency-domain vector is the standard frequency-domain vector in the target standard frequency-domain vector set, and the second formula is:

[0031]

[0032] In the formula, SML is the similarity coefficient, MDF(n) is the n-th element of the intermediate frequency-domain vector, SDF(n) is the n-th element of the target standard frequency-domain vector, and nmax is the total number of elements of the intermediate frequency-domain vector;

[0033] According to the discharge type of the candidate standard frequency-domain vector, add the plurality of similarity coefficients to the corresponding plurality of discharge type queues respectively, where each discharge type queue corresponds to a discharge type.

[0034] In a possible implementation manner, the construction process of the standard frequency-domain vector set includes:

[0035] Obtain an intermediate weight vector and a plurality of sample frequency-domain vectors, where each sample frequency-domain vector corresponds to a discharge type;

[0036] Cluster the multiple sample frequency-domain vectors into multiple sample classes with the number of discharge type categories;

[0037] Take the discharge type of the majority vectors in each sample class as the clustering discharge type;

[0038] Calculate the class centers of the multiple sample classes respectively to obtain multiple sample class centers;

[0039] Extract outlier vectors from each sample class to obtain multiple outlier vectors, where an outlier vector is a sample frequency-domain vector whose discharge type is different from the clustering discharge type of the class it belongs to;

[0040] If the ratio of the number of the multiple outlier vectors to the number of the multiple sample frequency-domain vectors is greater than the outlier quantity ratio threshold, adjust the intermediate weight vector according to the multiple outlier vectors, perform weight processing on each sample frequency-domain vector using the adjusted intermediate weight vector, take the sample frequency-domain vector after weight processing as the sample frequency-domain vector, and jump to the step of clustering the multiple sample frequency-domain vectors into multiple sample classes with the number of discharge type categories;

[0041] Otherwise, take the multiple target centers as the multiple standard frequency-domain vectors, take the intermediate weight vector as the first weight vector, and construct a standard frequency-domain vector set using the first weight vector and the multiple standard frequency-domain vectors.

[0042] In a possible implementation manner, the adjusting the intermediate weight vector according to the multiple outlier vectors and performing weight processing on each sample frequency-domain vector using the adjusted intermediate weight vector includes:

[0043] Select a sample class center from the multiple sample class centers as the target center according to the discharge type of each outlier vector to obtain multiple target centers, where the clustering discharge type of the class from which the target center is sourced is the same as the discharge type of the outlier vector;

[0044] Construct a weight optimization equation according to the multiple outlier vectors and the multiple sample class centers, where the weight optimization equation is:

[0045]

[0046] In the formula, SSDF i (n) is the nth element of the ith outlier vector, CDF i (n) is the nth element of the target center of the ith outlier vector, and W(n) is the nth element of the intermediate weight vector;

[0047] Adjust the intermediate weight vector according to the weight optimization equation;

[0048] Weight each sample frequency-domain vector using the adjusted intermediate weight vector.

[0049] In one possible implementation, determining the probabilities of multiple discharge types according to the multiple discharge type queues, and determining the discharge type of the discharge noise waveform according to the probabilities of the multiple discharge types includes:

[0050] Calculate the sum of multiple data in each discharge type queue to obtain multiple queue sums;

[0051] Determine multiple discharge probabilities according to the third formula and the multiple queue sums, where each discharge probability represents the probability of a discharge type, and the third formula is:

[0052]

[0053] In the formula, P(k) is the probability of the kth discharge type, R(k) is the queue sum of the kth discharge queue, and cnmax is the total number of discharge types;

[0054] Use the discharge type corresponding to the discharge probability with the largest value as the discharge type of the discharge noise waveform.

[0055] In a second aspect, an embodiment of the present invention provides a GIS device discharge type determination device for implementing the GIS device discharge type determination method described in the first aspect or any possible implementation of the first aspect above. The GIS device discharge type determination device includes:

[0056] A discharge noise waveform acquisition module for acquiring a discharge noise waveform;

[0057] A time-frequency domain feature extraction module for performing time-frequency domain feature extraction on the discharge noise waveform to obtain a time-frequency domain matrix;

[0058] A discharge type queue construction module for extracting multiple frequency-domain vectors from the time-frequency domain matrix and constructing multiple discharge type queues according to a standard frequency-domain vector set and the multiple frequency-domain vectors, where the standard frequency-domain vector set represents the discharge types corresponding to multiple standard frequency-domain vectors;

[0059] And,

[0060] A discharge type determination module for determining the probabilities of multiple discharge types according to the multiple discharge type queues, and determining the discharge type of the discharge noise waveform according to the probabilities of the multiple discharge types.

[0061] In a third aspect, an embodiment of the present invention provides an electronic device, including a memory and a processor. A computer program that can run on the processor is stored in the memory. When the processor executes the computer program, the steps of the method described in the first aspect above or any possible implementation manner of the first aspect are implemented.

[0062] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium. The computer-readable storage medium stores a computer program. When the computer program is executed by a processor, the steps of the method described in the first aspect above or any possible implementation manner of the first aspect are implemented.

[0063] The beneficial effects of the embodiments of the present invention compared with the prior art are as follows:

[0064] An embodiment of the present invention discloses a method for determining the discharge type of a GIS device. First, a discharge noise waveform is obtained; then, time-frequency domain feature extraction is performed on the discharge noise waveform to obtain a time-frequency domain matrix; next, a plurality of frequency domain vectors are extracted from the time-frequency domain matrix, and a plurality of discharge type queues are constructed according to a standard frequency domain vector set and the plurality of frequency domain vectors, where the standard frequency domain vector set represents the discharge types corresponding to a plurality of standard frequency domain vectors; finally, according to the plurality of discharge type queues, the probabilities of a plurality of discharge types are determined, and according to the probabilities of the plurality of discharge types, the discharge type of the discharge noise waveform is determined. In the embodiment of the present invention, by extracting the time-frequency domain matrix of the discharge noise waveform, extracting frequency domain vectors from the time-frequency domain matrix and performing similarity comparison with the standard frequency domain vector set, and determining the discharge type according to the comparison result, the standard frequency domain vector set requires fewer samples. Since the discharge type is determined by comparing with standard samples, the analysis result of the discharge type is more accurate and reliable. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0066] Figure 1 is a flowchart of the method for determining the discharge type of a GIS device provided by an embodiment of the present invention;

[0067] Figure 2 is a schematic diagram of the process of extracting frequency domain features by slidingly taking out a plurality of data segments from a discharge data set provided by an embodiment of the present invention;

[0068] Figure 3It is a schematic diagram of extracting heterogeneous vectors by clustering multiple sample frequency-domain vectors provided by an embodiment of the present invention;

[0069] Figure 4 It is a functional block diagram of a GIS equipment discharge type determination device provided by an embodiment of the present invention;

[0070] Figure 5 It is a functional block diagram of an electronic device provided by an embodiment of the present invention. Detailed implementation manners

[0071] In the following description, for the purpose of illustration rather than limitation, specific details such as specific system structures and technologies are presented in order to thoroughly understand the embodiments of the present invention. However, those skilled in the art should clearly understand that the present invention can also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, and methods are omitted to avoid unnecessary details from interfering with the description of the present invention.

[0072] To make the objectives, technical solutions, and advantages of the present invention clearer, the following will be described through specific implementation manners with reference to the accompanying drawings.

[0073] The following details the embodiments of the present invention. This example is implemented on the premise of the technical solution of the present invention, and detailed implementation manners and specific operation processes are given. However, the protection scope of the present invention is not limited to the following embodiments.

[0074] Figure 1 It is a flowchart of a method for determining the discharge type of a GIS equipment provided by an embodiment of the present invention.

[0075] As Figure 1 shown, it shows an implementation flowchart of a method for determining the discharge type of a GIS equipment provided by an embodiment of the present invention, which is described in detail as follows:

[0076] In step 101, a discharge noise waveform is acquired.

[0077] In step 102, time-frequency domain feature extraction is performed on the discharge noise waveform to obtain a time-frequency domain matrix.

[0078] In some embodiments, step 102 includes:

[0079] Discretize the discharge noise waveform to obtain a discharge data set;

[0080] Obtain a plurality of characteristic frequencies;

[0081] Successively take out the characteristic frequencies from the plurality of characteristic frequencies as the frequencies to be processed;

[0082] Slidingly take out a plurality of data segments from the discharge data set;

[0083] Perform frequency-domain feature extraction on the multiple data segments according to the to-be-processed frequency to obtain multiple frequency-domain features, where each frequency-domain feature corresponds to one data segment;

[0084] Construct the multiple frequency-domain features into a frequency-domain feature vector according to the positions of the multiple data segments in the discharge dataset;

[0085] Add the feature vector (frequency-domain feature vector) to the time-frequency domain matrix;

[0086] If the traversal of the multiple characteristic frequencies is not completed, jump to the step of sequentially taking out the characteristic frequencies from the multiple characteristic frequencies as the to-be-processed frequency.

[0087] In some embodiments, the performing frequency-domain feature extraction on the multiple data segments according to the to-be-processed frequency to obtain multiple frequency-domain features includes:

[0088] Perform frequency-domain feature extraction on the multiple data segments according to the first formula and the to-be-processed frequency to obtain multiple frequency-domain features, where the first formula is:

[0089]

[0090] In the formula, DF(n) is the nth frequency-domain feature, Dnmax is the total number of data in the first data segment, DS n (Dn) is the (Dn + 1)th data of the nth data segment, e is the natural constant, j is the imaginary unit, ω is the to-be-processed frequency, Tn is the number of data in the data segment within the period duration corresponding to the to-be-processed frequency, and π is the pi.

[0091] Exemplarily, in the embodiment of the present invention, based on the discharge noise waveform collected by the GIS device, a time-frequency domain matrix is extracted. Then, frequency-domain vectors are extracted from the time-frequency domain matrix. These extracted frequency vectors are compared with the standard frequency vectors in terms of similarity, and the obtained similarity values are added to the queue of the discharge types of the standard frequency vectors. According to the discharge type queue, the probability of each discharge type is calculated, and the discharge type is determined based on the probability of the discharge type.

[0092] Such as Figure 2As shown, the figure shows the schematic diagram of the process of extracting frequency-domain features by slidingly extracting multiple data segments from the discharge dataset provided by the embodiment of the present invention. In the aspect of constructing the time-frequency domain matrix in the embodiment of the present invention, first, data segments 202 are extracted from the discharge dataset 201 (the discharge dataset is obtained by discretizing the discharge noise waveform) in a sliding manner. Frequency-domain features 203 are respectively extracted from each data segment 202. According to the position of the data segment 202, the frequency-domain features 203 are constructed into a frequency-domain feature vector 204. Finally, frequency-domain vectors 204 in multiple different frequency domains are constructed into a time-frequency domain matrix.

[0093] In the aspect of extracting frequency-domain features, the embodiment of the present invention uses the first method, and the first formula is:

[0094]

[0095] In the formula, DF(n) is the nth frequency-domain feature, Dnmax is the total number of data in the first data segment, DS n (Dn) is the (Dn + 1)th data in the nth data segment, e is the natural constant, j is the imaginary unit, ω is the frequency to be processed, Tn is the number of data in the data segment within the period duration corresponding to the frequency to be processed, and π is the pi.

[0096] In step 103, multiple frequency-domain vectors are extracted from the time-frequency domain matrix, and multiple discharge type queues are constructed according to the standard frequency-domain vector set and the multiple frequency-domain vectors, where the standard frequency-domain vector set represents the discharge types corresponding to multiple standard frequency-domain vectors.

[0097] In some embodiments, step 103 includes:

[0098] Comparing the time-frequency domain matrix with multiple sample time-frequency domain matrices to extract multiple components, including:

[0099] Obtaining multiple standard frequency-domain vector sets, where each standard frequency-domain vector set corresponds to a frequency-domain frequency, and the standard frequency-domain vector set includes a first weight vector and multiple standard frequency-domain vectors, and each standard frequency-domain vector corresponds to a discharge type;

[0100] Traversingly extracting a frequency-domain vector from the time-frequency domain matrix as a frequency-domain vector to be processed, and performing the following steps after each extraction:

[0101] According to the frequency-domain frequency of the frequency-domain vector to be processed, extracting a target standard frequency-domain vector set from the multiple standard frequency-domain vector sets;

[0102] Performing weighted processing on the frequency-domain vector to be processed by using the target weight vector to obtain an intermediate frequency-domain vector, where the target weight vector is the first weight vector in the target standard frequency-domain vector set;

[0103] According to the second formula, determine the similarity coefficient between the intermediate frequency domain vector and each candidate standard frequency domain vector, so as to obtain a plurality of similarity coefficients, wherein the candidate standard frequency domain vector is the standard frequency domain vector in the target standard frequency domain vector set, and the second formula is:

[0104]

[0105] In the formula, SML is the similarity coefficient, MDF(n) is the nth element of the intermediate frequency domain vector, SDF(n) is the nth element of the target standard frequency domain vector, and nmax is the total number of elements of the intermediate frequency domain vector;

[0106] According to the discharge type of the candidate standard frequency domain vector, add the plurality of similarity coefficients to the corresponding plurality of discharge type queues respectively, wherein each discharge type queue corresponds to a discharge type.

[0107] In some embodiments, the construction process of the standard frequency domain vector set includes:

[0108] Obtain an intermediate weight vector and a plurality of sample frequency domain vectors, wherein each sample frequency domain vector corresponds to a discharge type;

[0109] Cluster the plurality of sample frequency domain vectors into a plurality of sample classes with the number of discharge type categories;

[0110] Take the discharge type of the majority vector in each sample class as the clustering discharge type;

[0111] Calculate the class centers of the plurality of sample classes respectively to obtain a plurality of sample class centers;

[0112] Extract the outlier vectors from each sample class to obtain a plurality of outlier vectors, wherein the outlier vector is a sample frequency domain vector whose discharge type is different from the clustering discharge type of the class where it is located;

[0113] If the ratio of the number of the plurality of outlier vectors to the number of the plurality of sample frequency domain vectors is greater than the outlier quantity ratio threshold, adjust the intermediate weight vector according to the plurality of outlier vectors, perform weight processing on each sample frequency domain vector by using the adjusted intermediate weight vector, take the weight-processed sample frequency domain vector as the sample frequency domain vector, and jump to the step of clustering the plurality of sample frequency domain vectors into a plurality of sample classes with the number of discharge type categories;

[0114] Otherwise, take the plurality of target centers as the plurality of standard frequency domain vectors, take the intermediate weight vector as the first weight vector, and construct a standard frequency domain vector set by using the first weight vector and the plurality of standard frequency domain vectors.

[0115] In some embodiments, adjusting the intermediate weight vector according to the plurality of heterogeneous vectors, and performing weight processing on each sample frequency domain vector by using the adjusted intermediate weight vector, includes:

[0116] Selecting a sample class center from the plurality of sample class centers as a target center according to the discharge type of each heterogeneous vector, so as to obtain a plurality of target centers, wherein the clustering discharge type of the source class of the target center is the same as the discharge type of the heterogeneous vector;

[0117] Constructing a weight optimization equation according to the plurality of heterogeneous vectors and the plurality of sample class centers, wherein the weight optimization equation is:

[0118]

[0119] In the formula, SSDF i (n) is the nth element of the i-th heterogeneous vector, CDF i (n) is the nth element of the target center of the i-th heterogeneous vector, and W(n) is the nth element of the intermediate weight vector;

[0120] Adjusting the intermediate weight vector according to the weight optimization equation;

[0121] Performing weight processing on each sample frequency domain vector by using the adjusted intermediate weight vector.

[0122] Exemplarily, in the embodiment of the present invention, a discharge type queue is constructed by comparing the similarity between the frequency domain vectors extracted from the time-frequency domain matrix and the standard frequency domain vectors in the standard frequency domain vector set.

[0123] Specifically, the standard frequency domain vector set includes a first weight vector and a plurality of standard frequency domain vectors. The first weight vector is used to process the frequency domain vectors extracted from the time-frequency domain matrix. In other words, the extracted frequency domain vectors are weighted to obtain intermediate frequency domain vectors. One way of weighting is to use the following formula:

[0124] MDF(n) = W(n) · DF(n)

[0125] In the formula, MDF(n) is the nth element of the intermediate frequency domain vector, W(n) is the nth element of the first weight vector, and DF(n) is the nth element of the frequency domain vector extracted from the time-frequency domain matrix.

[0126] Calculating the similarity between the obtained intermediate frequency domain vector and each standard frequency domain vector. The embodiment of this aspect applies the second formula:

[0127]

[0128] Wherein, SML is the similarity coefficient, MDF(n) is the nth element of the intermediate frequency domain vector, SDF(n) is the nth element of the target standard frequency domain vector, and nmax is the total number of elements of the intermediate frequency domain vector.

[0129] Finally, according to the discharge type of the standard frequency domain vector, the similarity coefficient obtained through the second formula is added to the discharge type queue.

[0130] Here, the construction process of the standard frequency domain vector set has to be explained. The standard frequency domain vector is constructed based on multiple sample frequency domain vectors. First, according to the number of types of discharge types, the sample frequency domain vectors are clustered. If the number of heterogeneous vectors in these classes is large, at this time, the sample frequency domain vectors need to be adjusted through the intermediate weight vector. After the adjustment, the above clustering process is repeated until the number of heterogeneous vectors reaches an acceptable level. At this time, the class center of the class obtained by clustering is calculated, and the class center is used as the standard frequency domain vector, and the intermediate weight vector used to adjust the sample frequency domain vector is used as the first weight vector.

[0131] Figure 3 It is a schematic diagram for extracting heterogeneous vectors by clustering multiple sample frequency domain vectors provided by the embodiment of the present invention. In the figure, multiple sample frequency domain vectors 302 respectively correspond to a discharge type. For example, the square is the first discharge type, the rectangle is the second discharge type, and the circle is the third discharge type. After these sample frequency domain vectors 302 are clustered, some vectors different from most discharge types will appear in each class 301, and these vectors are heterogeneous vectors 303.

[0132] And these heterogeneous vectors need to be optimized through the intermediate weight so that these heterogeneous vectors are clustered into the class where they should be (for example, Figure 3 the heterogeneous vectors in the rightmost class in

[0133] should be adjusted to the leftmost class).

[0134]

[0135] In the formula, SSDF i (n) is the nth element of the ith heterogeneous vector, CDF i (n) is the nth element of the target center of the ith heterogeneous vector, and W(n) is the nth element of the intermediate weight vector.

[0136] After the adjustment of the intermediate weight vector is completed, the intermediate weight vector is used to weight each sample frequency-domain vector (the aforementioned weight processing formula can be adopted). After the processing is completed, the clustering process is repeated. After several iterative loops of the above entire process, when the number of heterogeneous vectors reaches an acceptable level, the class center can be used as the standard frequency-domain vector, the discharge type of the class as the discharge type of the standard frequency-domain vector, and the intermediate weight vector as the first weight vector. The first weight vector and multiple standard frequency-domain vectors are used to construct a standard frequency-domain vector set.

[0137] In step 104, according to the multiple discharge type queues, the probabilities of multiple discharge types are determined, and according to the probabilities of the multiple discharge types, the discharge type of the discharge noise waveform is determined.

[0138] In some embodiments, step 104 includes:

[0139] Calculate the sum of multiple data in each discharge type queue to obtain multiple queue sums;

[0140] According to the third formula and the multiple queue sums, multiple discharge probabilities are determined, where each discharge probability represents the probability of a discharge type, and the third formula is:

[0141]

[0142] In the formula, P(k) is the probability of the k-th discharge type, R(k) is the queue sum of the k-th discharge queue, and cnmax is the total number of discharge types;

[0143] The discharge type corresponding to the discharge probability with the largest value is used as the discharge type of the discharge noise waveform.

[0144] Exemplarily, the sum obtained by summing the data in each discharge type queue represents the correlation degree of the discharge noise waveform. In the embodiments of the present invention, the probabilities of the discharge types are also calculated through these sums, and the third formula is applied:

[0145]

[0146] In the formula, P(k) is the probability of the k-th discharge type, R(k) is the queue sum of the k-th discharge queue, and cnmax is the total number of discharge types.

[0147] Generally, the discharge type with the largest value is selected from the above discharge type probabilities as the discharge type of the discharge noise waveform.

[0148] Embodiment of the method for determining the discharge type of GIS equipment of the present invention. First, a discharge noise waveform is acquired; then, time-frequency domain features of the discharge noise waveform are extracted to obtain a time-frequency domain matrix; next, multiple frequency domain vectors are extracted from the time-frequency domain matrix, and multiple discharge type queues are constructed based on a standard frequency domain vector set and the multiple frequency domain vectors, where the standard frequency domain vector set represents the discharge types corresponding to multiple standard frequency domain vectors; finally, based on the multiple discharge type queues, probabilities of multiple discharge types are determined, and based on the probabilities of the multiple discharge types, the discharge type of the discharge noise waveform is determined. In the embodiment of the present invention, by extracting the time-frequency domain matrix of the discharge noise waveform, frequency domain vectors are extracted from the time-frequency domain matrix and compared with the standard frequency domain vector set for similarity, and the discharge type is determined according to the comparison result. The standard frequency domain vector set requires fewer samples. Since the discharge type is determined by comparing with standard samples, the analysis result of the discharge type is more accurate and reliable.

[0149] It should be understood that the magnitudes of the sequence numbers of the steps in the above embodiments do not mean the order of execution. The order of execution of each process should be determined according to its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.

[0150] The following is the device embodiment of the present invention. For the details not described in detail therein, reference can be made to the corresponding method embodiment above.

[0151] Figure 4 is the functional block diagram of the device for determining the discharge type of GIS equipment provided by the embodiment of the present invention. Refer to Figure 4 The device for determining the discharge type of GIS equipment includes: a discharge noise waveform acquisition module 401, a time-frequency domain feature extraction module 402, a discharge type queue construction module 403, and a discharge type determination module 404, where:

[0152] The discharge noise waveform acquisition module 401 is configured to acquire a discharge noise waveform;

[0153] The time-frequency domain feature extraction module 402 is configured to extract time-frequency domain features of the discharge noise waveform to obtain a time-frequency domain matrix;

[0154] The discharge type queue construction module 403 is configured to extract multiple frequency domain vectors from the time-frequency domain matrix, and construct multiple discharge type queues based on a standard frequency domain vector set and the multiple frequency domain vectors, where the standard frequency domain vector set represents the discharge types corresponding to multiple standard frequency domain vectors;

[0155] The discharge type determination module 404 is configured to determine probabilities of multiple discharge types based on the multiple discharge type queues, and determine the discharge type of the discharge noise waveform based on the probabilities of the multiple discharge types.

[0156] Figure 5 is a functional block diagram of an electronic device provided by an embodiment of the present invention. As Figure 5 shown, the electronic device 5 of this embodiment includes: a processor 500 and a memory 501, and a computer program 502 that can run on the processor 500 is stored in the memory 501. When the processor 500 executes the computer program 502, the steps in the above-mentioned various GIS device discharge type determination methods and embodiments are implemented, for example Figure 1 the steps 101 to 104 shown.

[0157] Exemplarily, the computer program 502 can be divided into one or more modules / units, and the one or more modules / units are stored in the memory 501 and executed by the processor 500 to complete the present invention.

[0158] The electronic device 5 can be a computing device such as a desktop computer, a notebook, a palm computer, and a cloud server. The electronic device 5 may include, but is not limited to, a processor 500 and a memory 501. Those skilled in the art can understand that Figure 5 merely examples of the electronic device 5 do not constitute a limitation on the electronic device 5, and may include more or fewer components than shown in the figure, or combine certain components, or different components. For example, the electronic device 5 may further include input / output devices, network access devices, buses, etc.

[0159] The so-called processor 500 may be a central processing unit (CPU), or may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0160] The memory 501 may be an internal storage unit of the electronic device 5, such as a hard disk or memory of the electronic device 5. The memory 501 may also be an external storage device of the electronic device 5, such as a plug-in hard disk equipped on the electronic device 5, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, etc. Further, the memory 501 may also include both the internal storage unit and the external storage device of the electronic device 5. The memory 501 is used to store the computer program 502 and other programs and data required by the electronic device 5. The memory 501 may also be used to temporarily store data that has been output or is to be output.

[0161] Those skilled in the art can clearly understand that, for the convenience and conciseness of description, only the above division of each functional unit and module is used as an example. In actual applications, the above functions can be allocated to different functional units and modules according to needs, that is, the internal structure of the device is divided into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiment can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above integrated units can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of each functional unit and module are only for the convenience of mutual distinction and do not limit the protection scope of the present application. The specific working processes of the units and modules in the above system can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0162] In the above embodiments, the descriptions of the respective embodiments have their own emphases. For parts not detailed or recorded in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0163] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or by a combination of computer software and electronic hardware. Whether these functions are executed in hardware or software depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0164] In the embodiments provided by the present invention, it should be understood that the disclosed device / electronic device and method can be implemented in other ways. For example, the device / electronic device embodiments described above are merely illustrative. For example, the division of the modules or units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections between each other can be through some interfaces. The indirect couplings or communication connections of the devices or units can be in electrical, mechanical or other forms.

[0165] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0166] In addition, in each embodiment of the present invention, the functional units can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above-mentioned integrated units can be implemented in the form of hardware or in the form of software functional units.

[0167] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above-described method embodiments of the present invention can also be completed by a computer program instructing relevant hardware. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above-described various method and device embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file or some intermediate form, etc. The computer-readable medium can include: any entity or device that can carry the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc.

[0168] The above-described embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the same; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention and should all be included within the protection scope of the present invention.

Claims

1. A method for determining the discharge type of GIS equipment, characterized in that: include: Obtain discharge noise waveform; Extracting time-frequency domain features of the discharge noise waveform to obtain a time-frequency domain matrix; Extracting multiple frequency domain vectors from the time-frequency domain matrix, and constructing multiple discharge type queues according to a standard frequency domain vector set and the multiple frequency domain vectors, wherein the standard frequency domain vector set represents the discharge types corresponding to the multiple standard frequency domain vectors; Determining the probabilities of a plurality of discharge types according to the plurality of discharge type queues, and determining the discharge type of the discharge noise waveform according to the probabilities of the plurality of discharge types; The step of extracting a plurality of frequency domain vectors from the time-frequency domain matrix and constructing a plurality of discharge type queues according to a standard frequency domain vector set and the plurality of frequency domain vectors includes: Acquire multiple standard frequency domain vector sets, wherein each standard frequency domain vector set corresponds to a frequency domain frequency, the standard frequency domain vector set includes a first weight vector and multiple standard frequency domain vectors, and each standard frequency domain vector corresponds to a discharge type; The frequency domain vectors are traversally extracted from the time-frequency domain matrix as the frequency domain vectors to be processed, and the following steps are performed after each extraction: Extracting a target standard frequency domain vector set from the plurality of standard frequency domain vector sets according to the frequency domain frequency of the frequency domain vector to be processed; Using a target weight vector to perform weighting processing on the frequency domain vector to be processed to obtain an intermediate frequency domain vector, wherein the target weight vector is the first weight vector in the target standard frequency domain vector set; According to the second formula, a similarity coefficient between the intermediate frequency domain vector and each candidate standard frequency domain vector is determined, thereby obtaining multiple similarity coefficients, wherein the candidate standard frequency domain vector is a standard frequency domain vector in the target standard frequency domain vector set, and the second formula is: In the formula, is the similarity coefficient, is the intermediate frequency domain vector elements, is the first vector of the target standard frequency domain elements, is the total number of intermediate frequency domain vector elements; According to the discharge type of the candidate standard frequency domain vector, the multiple similarity coefficients are respectively added to the corresponding multiple discharge type queues, wherein each discharge type queue corresponds to one discharge type.

2. The method for determining the discharge type of GIS equipment according to claim 1, characterized in that: The step of extracting time-frequency domain features of the discharge noise waveform to obtain a time-frequency domain matrix includes: discretizing the discharge noise waveform to obtain a discharge data set; Get multiple characteristic frequencies; From the plurality of characteristic frequencies, sequentially taking characteristic frequencies as frequencies to be processed; Slip-taking a plurality of data segments from the discharge data set; Extracting frequency domain features from the multiple data segments according to the frequencies to be processed to obtain multiple frequency domain features, wherein each frequency domain feature corresponds to one data segment; constructing the plurality of frequency domain features into a frequency domain feature vector according to the positions of the plurality of data segments in the discharge data set; Adding the eigenvector frequency domain eigenvector to the time-frequency domain matrix; If the traversal of the multiple characteristic frequencies is not completed, the process jumps to the step of sequentially taking out characteristic frequencies from the multiple characteristic frequencies as frequencies to be processed.

3. The method for determining the discharge type of GIS equipment according to claim 2, characterized in that: The extracting frequency domain features of the multiple data segments according to the frequencies to be processed to obtain multiple frequency domain features includes: Frequency domain features are extracted from the multiple data segments according to the first formula and the frequency to be processed to obtain multiple frequency domain features, wherein the first formula is: In the formula, For the frequency domain features, is the total amount of data in the first data segment, For the The first data segment data, is a natural constant, is an imaginary unit, is the frequency to be processed, is the number of data in the data segment within the period length corresponding to the frequency to be processed, is the ratio of pi.

4. The method for determining the discharge type of GIS equipment according to claim 1, characterized in that: The construction process of the standard frequency domain vector set includes: Obtaining an intermediate weight vector and a plurality of sample frequency domain vectors, wherein each sample frequency domain vector corresponds to a discharge type; Clustering the plurality of sample frequency domain vectors into a plurality of sample classes as many as the number of discharge types; The discharge type of the majority vector in each sample class is taken as the cluster discharge type; Calculating the class centers of the multiple sample classes respectively to obtain multiple sample class centers; Extracting heterogeneous vectors from each sample class, thereby obtaining a plurality of heterogeneous vectors, wherein the heterogeneous vectors are sample frequency domain vectors whose discharge type is different from the cluster discharge type of the class to which they belong; If the ratio of the number of the plurality of heterogeneous vectors to the number of the plurality of sample frequency domain vectors is greater than the heterogeneous number ratio threshold, the intermediate weight vector is adjusted according to the plurality of heterogeneous vectors, each sample frequency domain vector is weighted by using the adjusted intermediate weight vector, the sample frequency domain vector after weighting is used as the sample frequency domain vector, and the process jumps to the step of clustering the plurality of sample frequency domain vectors into a plurality of sample classes of the number of discharge types; Otherwise, the multiple sample class centers are used as the multiple standard frequency domain vectors, the intermediate weight vector is used as the first weight vector, and a standard frequency domain vector set is constructed using the first weight vector and the multiple standard frequency domain vectors.

5. The method for determining the discharge type of GIS equipment according to claim 4, characterized in that: The step of adjusting the intermediate weight vector according to the multiple heterogeneous vectors and performing weighting processing on each sample frequency domain vector using the adjusted intermediate weight vector includes: According to the discharge type of each heterogeneous vector, a sample class center is selected from the multiple sample class centers as a target center, thereby obtaining multiple target centers, wherein the cluster discharge type of the source class of the target center is the same as the discharge type of the heterogeneous vector; A weight optimization equation is constructed according to the multiple heterogeneous vectors and the multiple sample class centers, wherein the weight optimization equation is: In the formula, For the The heterogeneous vector elements, For the The target center of the heterogeneous vector elements, is the intermediate weight vector elements; According to the weight optimization equation, adjusting the intermediate weight vector; Each sample frequency domain vector is weighted using the adjusted intermediate weight vector.

6. The method for determining the discharge type of GIS equipment according to any one of claims 1 to 5, characterized in that: The determining the probabilities of a plurality of discharge types according to the plurality of discharge type queues, and determining the discharge type of the discharge noise waveform according to the probabilities of the plurality of discharge types, comprises: Calculate the sum of multiple data in each discharge type queue, thereby obtaining multiple queue sums; According to the third formula and the sum of the multiple queues, multiple discharge probabilities are determined, wherein each discharge probability represents the probability of a discharge type, and the third formula is: In the formula, For the The probability of the discharge type, For the The sum of the discharge queues, For the The sum of the discharge queues, is the total number of discharge types; The discharge type corresponding to the discharge probability with the largest value is used as the discharge type of the discharge noise waveform.

7. A device for determining the discharge type of GIS equipment, characterized in that: Used to implement the method for determining the discharge type of GIS equipment according to any one of claims 1 to 6, the GIS equipment discharge type determination device comprises: A discharge noise waveform acquisition module, used to acquire a discharge noise waveform; A time-frequency domain feature extraction module, used to extract the time-frequency domain features of the discharge noise waveform to obtain a time-frequency domain matrix; A discharge type queue construction module is used to extract multiple frequency domain vectors from the time-frequency domain matrix, and construct multiple discharge type queues according to a standard frequency domain vector set and the multiple frequency domain vectors, wherein the standard frequency domain vector set represents the discharge types corresponding to the multiple standard frequency domain vectors; as well as, The discharge type determination module is used to determine the probabilities of multiple discharge types according to the multiple discharge type queues, and determine the discharge type of the discharge noise waveform according to the probabilities of the multiple discharge types.

8. An electronic device comprising a memory and a processor, wherein the memory stores a computer program that can be run on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

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

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