CN112034312A - Power equipment insulation defect mode identification method - Google Patents
Power equipment insulation defect mode identification method Download PDFInfo
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- CN112034312A CN112034312A CN202010791030.4A CN202010791030A CN112034312A CN 112034312 A CN112034312 A CN 112034312A CN 202010791030 A CN202010791030 A CN 202010791030A CN 112034312 A CN112034312 A CN 112034312A
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- 238000000034 method Methods 0.000 title claims abstract description 40
- 230000007547 defect Effects 0.000 title claims abstract description 33
- 238000009413 insulation Methods 0.000 title claims abstract description 31
- 238000012360 testing method Methods 0.000 claims abstract description 16
- 238000012549 training Methods 0.000 claims abstract description 10
- 239000011159 matrix material Substances 0.000 claims description 21
- 238000001228 spectrum Methods 0.000 claims description 12
- 238000009432 framing Methods 0.000 claims description 3
- 238000012567 pattern recognition method Methods 0.000 claims description 3
- 238000012545 processing Methods 0.000 claims description 3
- 238000005070 sampling Methods 0.000 claims description 3
- 238000003745 diagnosis Methods 0.000 abstract description 8
- 238000001514 detection method Methods 0.000 abstract description 7
- 230000000694 effects Effects 0.000 description 3
- 239000002923 metal particle Substances 0.000 description 3
- 239000000725 suspension Substances 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 239000002184 metal Substances 0.000 description 1
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/12—Testing dielectric strength or breakdown voltage ; Testing or monitoring effectiveness or level of insulation, e.g. of a cable or of an apparatus, for example using partial discharge measurements; Electrostatic testing
- G01R31/1209—Testing dielectric strength or breakdown voltage ; Testing or monitoring effectiveness or level of insulation, e.g. of a cable or of an apparatus, for example using partial discharge measurements; Electrostatic testing using acoustic measurements
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/12—Testing dielectric strength or breakdown voltage ; Testing or monitoring effectiveness or level of insulation, e.g. of a cable or of an apparatus, for example using partial discharge measurements; Electrostatic testing
- G01R31/1227—Testing dielectric strength or breakdown voltage ; Testing or monitoring effectiveness or level of insulation, e.g. of a cable or of an apparatus, for example using partial discharge measurements; Electrostatic testing of components, parts or materials
- G01R31/1254—Testing dielectric strength or breakdown voltage ; Testing or monitoring effectiveness or level of insulation, e.g. of a cable or of an apparatus, for example using partial discharge measurements; Electrostatic testing of components, parts or materials of gas-insulated power appliances or vacuum gaps
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
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- G06F17/14—Fourier, Walsh or analogous domain transformations, e.g. Laplace, Hilbert, Karhunen-Loeve, transforms
- G06F17/141—Discrete Fourier transforms
- G06F17/142—Fast Fourier transforms, e.g. using a Cooley-Tukey type algorithm
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Abstract
The invention discloses a power equipment insulation defect mode identification method, which relates to the field of GIS insulation defect fault diagnosis, and comprises the following steps: collecting an ultrasonic signal of GIS partial discharge; extracting MFCC characteristic parameters from the ultrasonic signals and constructing an MFCC two-dimensional map; dividing the sample into a training set and a test set according to a proportion, constructing an over-complete dictionary by using the training set, and performing sparse reconstruction on the test set by adopting an OMP (open unified platform protocol) method according to the established over-complete dictionary; and judging the discharge type according to the accumulated error of the non-zero item. The method is based on the MFCC characteristic map and OMP sparse reconstruction, realizes intelligent identification of GIS insulation defects, and improves the intelligent level of a GIS partial discharge detection system.
Description
Technical Field
The invention relates to the field of GIS insulation defect fault diagnosis, in particular to a power equipment insulation defect mode identification method.
Background
A Gas Insulated Switchgear (GIS) is a Gas Insulated metal enclosed Switchgear with SF6 Gas as the insulating medium. Compared with the traditional open-type transformer substation, the GIS has the remarkable advantages of small occupied area, high operation reliability, strong safety, long maintenance period and the like. Therefore, since the practical use of the 20 th century in the 60 th era, GIS has been widely used in domestic and foreign electric power systems.
Because of the wide application and importance of GIS in the power grid, its operation condition is closely related to whether the whole power grid can work normally and safely. The GIS partial discharge is monitored on line, so that negative effects caused by shutdown can be avoided while the insulation condition of the GIS partial discharge is mastered, the GIS current insulation state can be more represented by detection in a non-shutdown state, and the GIS partial discharge online monitoring method has important significance for ensuring safe and stable operation of the whole power system. Ultrasonic detection is a commonly used partial discharge detection method at present, but the method for identifying insulation defects based on ultrasonic detection partial discharge signals is low in accuracy, and accurate diagnosis of GIS insulation defect categories cannot be achieved.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to overcome the defects in the prior art and provide a GIS partial discharge fault diagnosis method, which is based on an MFCC characteristic map and OMP sparse reconstruction, realizes intelligent identification of GIS insulation defects and improves the intelligent level of a GIS partial discharge detection system.
In order to achieve the purpose, the technical scheme of the invention is as follows:
an electrical equipment insulation defect pattern recognition method, comprising:
step 1: collecting an ultrasonic signal of GIS partial discharge;
step 2: extracting MFCC characteristic parameters from the ultrasonic signals and constructing an MFCC two-dimensional map;
and step 3: dividing the sample into a training set and a test set according to a proportion, constructing an over-complete dictionary by using the training set, and performing sparse reconstruction on the test set by adopting an OMP (open unified platform protocol) method according to the established over-complete dictionary;
and 4, step 4: and judging the discharge type according to the accumulated error of the non-zero item.
As mentioned above, the method for identifying insulation defect pattern of electrical equipment further includes the following specific steps:
step 2-1: windowing the signal frame: taking the frame length as the length of a power frequency period, namely 20ms, and the overlapping rate as 50%, and adding a Hamming window to the signal to prevent the frequency spectrum leakage in the subsequent process;
step 2-2: performing FFT on the windowed signal;
step 2-3: the signal is filtered using a Mel filter bank, whose frequency response is as follows:
wherein f (M) is the center frequency of the mth triangular filter, and M triangular filters are used in total;
step 2-4: logarithmic energy processing is carried out on the Mel frequency spectrum to obtain a logarithmic frequency spectrum;
where X (k) is FFT, H of the framed signalm(k) Is the Mel-filter frequency response, N is the FFT length;
step 2-5: discrete cosine transform is carried out on the logarithmic spectrum to obtain M MFCC characteristic parameters;
step 2-6: after MFCC characteristic parameters of the signals are obtained, the characteristic matrixes are arranged according to the framing sequence to form a characteristic matrix, and finally a two-dimensional MFCC characteristic map of the signals is obtained.
In the method for identifying the insulation defect pattern of the power equipment, further, the performing sparse reconstruction on the test set by the OMP method specifically includes:
the input quantity of the OMP comprises a characteristic quantity matrix X, a sensing matrix phi, a sampling vector y and sparsity K; k-sparse approximation of X-output
Step 3-1: initialization parameter, residual r0Y, index set Λ0Phi, the iteration time t is 1, and the maximum iteration time M;
step 3-2: by passingCalculating residual r and columns of the sensing matrix
Inner product of, obtain the corner mark λ of the maximum value, i.e.
Step 3-3: from λtUpdate index set Λt＝Λt-1∪{λtRecording the set of reconstructed atoms in the found sensing matrix
Step 3-4: obtained by least squares
Step 3-5: updating residual errors
Step 3-6: the step 3-1 to the step 3-5 are circulated until the iteration frequency is judged whether t is more than or equal to M, if so, the iteration is stopped, a non-zero accumulated error, namely a final residual value, is obtained, and the type with the minimum residual value is the insulation defect type of the sample to be identified; if not, returning to execute the step 3-1.
Compared with the prior art, the invention has the beneficial effects that: the method comprises the steps of firstly extracting MFCC voiceprint characteristic parameters of ultrasonic partial discharge signals, constructing to form an MFCC characteristic map, secondly constructing an over-complete dictionary by using samples, carrying out sparse reconstruction on a sample to be detected by adopting an OMP (open metering protocol) method based on the established over-complete dictionary, and finally judging the discharge type according to the accumulated error of non-zero items, thereby realizing intelligent identification of GIS insulation defects and improving the intelligent level of a GIS partial discharge detection system. .
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In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a defect identification flow chart based on OMP sparse reconstruction;
FIG. 2 is GIS partial discharge ultrasonic signals under different insulation defects, wherein FIG. 2(a) is metal particles, and FIG. 2(b) is suspension potential; FIG. 2(c) is a creeping discharge; FIG. 2(d) sharp plate discharge;
FIG. 3 is a MFCC signature for various insulation defects, where FIG. 3(a) is the metal particle and FIG. 3(b) is the floating potential; FIG. 3(c) is a creeping discharge; FIG. 3(d) shows a sharp plate discharge.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and detailed description.
Example (b):
the technical solutions in the embodiments of the present invention will be described clearly and completely with reference to the accompanying drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The invention provides a GIS insulation defect diagnosis and identification method based on MFCC characteristic map and OMP sparse reconstruction based on the characteristics of GIS partial discharge ultrasonic signals. Firstly, extracting MFCC voiceprint characteristic parameters of an ultrasonic partial discharge signal, constructing to form an MFCC characteristic map, secondly, constructing an over-complete dictionary by using a sample, carrying out sparse reconstruction on the sample to be detected by adopting an OMP (open unified modeling) method based on the established over-complete dictionary, and finally, judging the discharge type according to the accumulated error of a non-zero term. The application effect of the identification method in GIS insulation defect identification diagnosis is verified through testing a large amount of test data.
Referring to fig. 1, fig. 1 is a flowchart of defect identification based on OMP sparse reconstruction, and the method may include the following steps:
step 1: and acquiring an ultrasonic signal of GIS partial discharge by using an ultrasonic sensor.
Step 2: and extracting MFCC characteristic parameters from the ultrasonic signals, and constructing an MFCC two-dimensional map.
And step 3: and dividing the sample into a training set and a test set according to a proportion, constructing an over-complete dictionary by using the training set, and performing sparse reconstruction on the test set by adopting an OMP (open unified platform protocol) method according to the established over-complete dictionary.
And 4, step 4: and judging the discharge type according to the accumulated error of the non-zero item.
Mel-frequency cepstral coefficients (MFCCs) are the voiceprint characteristics that convert the actual frequency into the Mel-frequency domain to obtain an acoustic signal. The relationship between the Mel frequency and the actual frequency is
In the formula (f)mekThe Mel frequency, and f the actual frequency.
The specific process for establishing the MFCC characteristic map comprises the following steps:
step 2-1: windowing the signal frame: taking the frame length as the length of a power frequency period, namely 20ms, and the overlapping rate as 50%, and adding a Hamming window to the signal to prevent the frequency spectrum leakage in the subsequent process;
step 2-2: performing FFT on the windowed signal;
step 2-3: the signal is filtered using a Mel filter bank, whose frequency response is as follows:
wherein f (M) is the center frequency of the mth triangular filter, and M triangular filters are used in total;
step 2-4: logarithmic energy processing is carried out on the Mel frequency spectrum to obtain a logarithmic frequency spectrum;
where X (k) is FFT, H of the framed signalm(k) Is the Mel-filter frequency response, N is the FFT length;
step 2-5: discrete cosine transform is carried out on the logarithmic spectrum to obtain M MFCC characteristic parameters;
step 2-6: after MFCC characteristic parameters of the signals are obtained, the characteristic matrixes are arranged according to the framing sequence to form a characteristic matrix, and finally a two-dimensional MFCC characteristic map of the signals is obtained.
The basic idea of Orthogonal Matching Pursuit (OMP) is to select a column with the largest correlation (inner product) with the measured signal from the holographic matrix in the process of each iteration, remove the column from the holographic matrix and add the column into the expanded matrix, then use the principle of least square method to calculate the estimation which minimizes the residual error, and then continuously subtract the correlation column from the holographic matrix to repeat the above process until reaching the specified number of iterations or reaching the sparsity requirement.
The input quantity of the OMP comprises a characteristic quantity matrix X, a sensing matrix phi, a sampling vector y and sparsity K; k-sparse approximation of X-output
The method comprises the following specific steps:
step 3-1: initialization parameter, residual r0Y, index set Λ0Phi, the iteration time t is 1, and the maximum iteration time M;
step 3-2: by calculating the residual r and the columns of the sensing matrix
Inner product of, obtain the corner mark λ of the maximum value, i.e.
Step 3-3: from λtUpdate index set Λt＝Λt-1∪{λtRecording the set of reconstructed atoms in the found sensing matrix
Step 3-4: obtained by least squares
Step 3-5: updating residual errors
Step 3-6: the step 3-1 to the step 3-5 are circulated until the iteration frequency is judged whether t is more than or equal to M, if so, the iteration is stopped, a non-zero accumulated error, namely a final residual value, is obtained, and the type with the minimum residual value is the insulation defect type of the sample to be identified; if not, returning to execute the step 3-1.
In order to verify the effectiveness of the method provided by the patent, four GIS insulation defect models, namely metal particle discharge, suspension discharge, sharp plate discharge and creeping discharge, are established in a laboratory, ultrasonic partial discharge signals of the GIS insulation defect models are collected, and the collected original signals are shown in figure 2.
And then analyzing and diagnosing the acquired ultrasonic signals according to the identification method. The MFCC characteristic parameters of each group of signals are extracted, an MFCC characteristic matrix is formed, the matrix is imaged, and the obtained MFCC characteristic map is shown in FIG. 3.
Randomly selected 70% of samples as training group and 30% of samples as testing group. And (3) constructing an over-complete dictionary by using the training set, performing sparse reconstruction on the over-complete dictionary by using an OMP (object model program) method on the test set, judging the seed discharge type according to the minimum residual error, and counting the final recognition result as shown in the following table 1, wherein the average recognition accuracy rate reaches 91.67%.
TABLE 1 OMP sparse reconstruction insulation Defect identification results
In order to embody the superiority of the method, an SVM model and an ANN model are simultaneously established to identify the MFCC characteristic map, and the identification accuracy statistics of different identification methods are as follows in the following table 2:
TABLE 2 recognition results of different models
Comparing the recognition accuracy results of different recognition models in table 2, the recognition accuracy of the OMP sparse reconstruction model to the MFCC feature map is higher than that of the SVM and the ANN, and the effectiveness of the method provided by the patent is verified.
The invention provides a GIS insulation defect diagnosis and identification method based on MFCC characteristic map and OMP sparse reconstruction based on the characteristics of GIS partial discharge ultrasonic signals. Firstly, extracting MFCC voiceprint characteristic parameters of an ultrasonic partial discharge signal, constructing to form an MFCC characteristic map, secondly, constructing an over-complete dictionary by using a sample, carrying out sparse reconstruction on the sample to be detected by adopting an OMP (open unified modeling) method based on the established over-complete dictionary, and finally, judging the discharge type according to the accumulated error of a non-zero term. The application effect of the identification method in GIS insulation defect identification diagnosis is verified through testing a large amount of test data.
The above embodiments are only for illustrating the technical concept and features of the present invention, and the purpose thereof is to enable those skilled in the art to understand the contents of the present invention and implement the present invention accordingly, and not to limit the protection scope of the present invention accordingly. All equivalent changes or modifications made in accordance with the spirit of the present disclosure are intended to be covered by the scope of the present disclosure.
Claims (3)
1. An electrical equipment insulation defect pattern recognition method is characterized by comprising the following steps:
step 1: collecting an ultrasonic signal of GIS partial discharge;
step 2: extracting MFCC characteristic parameters from the ultrasonic signals and constructing an MFCC two-dimensional map;
and step 3: dividing the sample into a training set and a test set according to a proportion, constructing an over-complete dictionary by using the training set, and performing sparse reconstruction on the test set by adopting an OMP (open unified platform protocol) method according to the established over-complete dictionary;
and 4, step 4: and judging the discharge type according to the accumulated error of the non-zero item.
2. The electrical equipment insulation defect pattern recognition method of claim 1, wherein constructing the MFCC two-dimensional map specifically comprises:
step 2-1: windowing the signal frame: taking the frame length as the length of a power frequency period, namely 20ms, and the overlapping rate as 50%, and adding a Hamming window to the signal to prevent the frequency spectrum leakage in the subsequent process;
step 2-2: performing FFT on the windowed signal;
step 2-3: the signal is filtered using a Mel filter bank, whose frequency response is as follows:
wherein f (M) is the center frequency of the mth triangular filter, and M triangular filters are used in total;
step 2-4: logarithmic energy processing is carried out on the Mel frequency spectrum to obtain a logarithmic frequency spectrum;
where X (k) is FFT, H of the framed signalm(k) Is the Mel-filter frequency response, N is the FFT length;
step 2-5: discrete cosine transform is carried out on the logarithmic spectrum to obtain M MFCC characteristic parameters;
step 2-6: after MFCC characteristic parameters of the signals are obtained, the characteristic matrixes are arranged according to the framing sequence to form a characteristic matrix, and finally a two-dimensional MFCC characteristic map of the signals is obtained.
3. The method for identifying the insulation defect mode of the power equipment according to claim 1, wherein the OMP method for sparsely reconstructing the test set specifically comprises the following steps:
the input quantity of the OMP comprises a characteristic quantity matrix X, a sensing matrix phi, a sampling vector y and sparsity K; k-sparse approximation of X-output
Step 3-1: initialization parameter, residual r0Y, index set Λ0Phi, the iteration time t is 1, and the maximum iteration time M;
step 3-2: by calculating the residual r and the columns of the sensing matrix
Inner product of, obtain the corner mark λ of the maximum value, i.e.
Step 3-3: from λtUpdate index set Λt＝Λt-1∪{λtRecording the set of reconstructed atoms in the found sensing matrix
Step 3-4: obtained by least squares
Step 3-5: updating residual errors
Step 3-6: the step 3-1 to the step 3-5 are circulated until the iteration frequency is judged whether t is more than or equal to M, if so, the iteration is stopped, a non-zero accumulated error, namely a final residual value, is obtained, and the type with the minimum residual value is the insulation defect type of the sample to be identified; if not, returning to execute the step 3-1.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112946442A (en) * | 2021-04-12 | 2021-06-11 | 厦门理工学院 | Switch cabinet partial discharge detection method, terminal equipment and storage medium |
| CN115097266A (en) * | 2022-06-20 | 2022-09-23 | 国网上海市电力公司 | Power cable partial discharge type identification method and device and storage medium |
| CN117630611A (en) * | 2024-01-22 | 2024-03-01 | 南京卓煊电力科技有限公司 | Full-bandwidth high-frequency partial discharge PRPD spectrogram capturing and generating method and system |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102426835A (en) * | 2011-08-30 | 2012-04-25 | 华南理工大学 | Switch cabinet partial discharge signal identification method based on support vector machine model |
| CN107293302A (en) * | 2017-06-27 | 2017-10-24 | 苏州大学 | A kind of sparse spectrum signature extracting method being used in voice lie detection system |
| CN108231067A (en) * | 2018-01-13 | 2018-06-29 | 福州大学 | Sound scenery recognition methods based on convolutional neural networks and random forest classification |
| CN109815357A (en) * | 2019-01-28 | 2019-05-28 | 辽宁工程技术大学 | A kind of remote sensing image retrieval method based on Nonlinear Dimension Reduction and rarefaction representation |
| CN109856517A (en) * | 2019-03-29 | 2019-06-07 | 国家电网有限公司 | A kind of method of discrimination of extra-high voltage equipment Partial Discharge Detection data |
| CN110490071A (en) * | 2019-07-12 | 2019-11-22 | 国网上海市电力公司 | A kind of substation's Abstraction of Sound Signal Characteristics based on MFCC |
-
2020
- 2020-08-07 CN CN202010791030.4A patent/CN112034312A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102426835A (en) * | 2011-08-30 | 2012-04-25 | 华南理工大学 | Switch cabinet partial discharge signal identification method based on support vector machine model |
| CN107293302A (en) * | 2017-06-27 | 2017-10-24 | 苏州大学 | A kind of sparse spectrum signature extracting method being used in voice lie detection system |
| CN108231067A (en) * | 2018-01-13 | 2018-06-29 | 福州大学 | Sound scenery recognition methods based on convolutional neural networks and random forest classification |
| CN109815357A (en) * | 2019-01-28 | 2019-05-28 | 辽宁工程技术大学 | A kind of remote sensing image retrieval method based on Nonlinear Dimension Reduction and rarefaction representation |
| CN109856517A (en) * | 2019-03-29 | 2019-06-07 | 国家电网有限公司 | A kind of method of discrimination of extra-high voltage equipment Partial Discharge Detection data |
| CN110490071A (en) * | 2019-07-12 | 2019-11-22 | 国网上海市电力公司 | A kind of substation's Abstraction of Sound Signal Characteristics based on MFCC |
Non-Patent Citations (5)
| Title |
|---|
| QINQIN ZHANG ET AL.: "Fault Identification Based on PD Ultrasonic Signal Using RNN, DNN and CNN", 《 2018 CONDITION MONITORING AND DIAGNOSIS (CMD)》 * |
| 刘云鹏 等: "基于稀疏表示的绝缘子紫外图谱闪络状态分类评估方法", 《高电压技术》 * |
| 张思建 等: "音频事件检测算法在计量费控用电压开关柜局部放电检测中的应用研究", 《电子器件》 * |
| 杨丰源等: "基于压缩感知的高压直流电缆局部放电模式识别", 《高电压技术》 * |
| 薛海双: "基于MCFF字典和SL0算法的语音压缩感知研究", 《中国优秀硕士学位论文全文数据库 信息科技辑》 * |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112946442A (en) * | 2021-04-12 | 2021-06-11 | 厦门理工学院 | Switch cabinet partial discharge detection method, terminal equipment and storage medium |
| CN112946442B (en) * | 2021-04-12 | 2022-06-28 | 厦门理工学院 | Switch cabinet partial discharge detection method, terminal equipment and storage medium |
| CN115097266A (en) * | 2022-06-20 | 2022-09-23 | 国网上海市电力公司 | Power cable partial discharge type identification method and device and storage medium |
| CN117630611A (en) * | 2024-01-22 | 2024-03-01 | 南京卓煊电力科技有限公司 | Full-bandwidth high-frequency partial discharge PRPD spectrogram capturing and generating method and system |
| CN117630611B (en) * | 2024-01-22 | 2024-04-12 | 南京卓煊电力科技有限公司 | Full-bandwidth high-frequency partial discharge PRPD spectrogram capturing and generating method and system |
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