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CN118643278B - TEV pulse sampling counting method - Google Patents

TEV pulse sampling counting method Download PDF

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CN118643278B
CN118643278B CN202411117990.7A CN202411117990A CN118643278B CN 118643278 B CN118643278 B CN 118643278B CN 202411117990 A CN202411117990 A CN 202411117990A CN 118643278 B CN118643278 B CN 118643278B
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data points
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tev
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CN118643278A (en
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管保柱
张鑫
吴路明
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Nanjing Fuhua Xinneng Technology Co ltd
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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
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/10—Pre-processing; Data cleansing
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/22—Matching criteria, e.g. proximity measures
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/23—Clustering techniques
    • G06F18/232—Non-hierarchical techniques
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/243—Classification techniques relating to the number of classes
    • G06F18/2433—Single-class perspective, e.g. one-against-all classification; Novelty detection; Outlier detection
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
    • G06F2218/02—Preprocessing
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
    • G06F2218/12—Classification; Matching

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Abstract

The invention discloses a TEV pulse sampling counting method, which relates to the technical field of pulse sampling counting methods and comprises the following steps of collecting TEV signals under undersampling condition, setting the sampling rate to be 250K, downsampling the collected TEV data to 5000 data points, clustering the data points in the PRPD drawing drawn by the TEV data through a DBSCAN algorithm based on the PRPD drawing drawn by the TEV data, wherein one type is background noise, the other type is potential partial discharge point, eliminating the background noise data points, and reserving the potential partial discharge point. The invention solves the problem of how to realize statistics of the number of TEV pulses under the undersampling condition, has important significance for miniaturization and practicability of equipment based on a TEV detection mode, remarkably improves the accuracy and comprehensiveness of interference point removal, and can better count the number of the TEV pulses.

Description

TEV pulse sampling counting method
Technical Field
The invention relates to the technical field of pulse sampling and counting methods, in particular to a TEV pulse sampling and counting method.
Background
The DBSCAN is a clustering algorithm based on density, and compared with the traditional K-Means algorithm, the DBSCAN can find out clustering clusters with arbitrary shapes, can find out abnormal points during clustering, and has a good effect on dense data aggregation. In the detection method based on the TEV partial discharge, the TEV count is an important reference basis, and the severity of the partial discharge can be intuitively judged according to the pulse number. However, since the frequency bands of the TEV signals are distributed in 3-100MHZ, it is difficult to combine distortion-free sampling with sampling equipment with smaller size, so that the current mainstream practice is to collect the TEV signals in an undersampling manner, but this will cause failure of various mature signal analysis methods, which is difficult to use, and how to calculate the number of TEV pulses accurately under the undersampling condition becomes a great difficulty.
In the sine wave voltage waveform, 90 degrees and 270 degrees represent the instants at which the voltage waveform changes from a negative peak to a positive peak (or vice versa), respectively. Near the two phase points, the instantaneous value of the voltage changes faster, so that the concentration and enhancement of a local electric field in an insulation system are easy to cause local discharge, namely the local discharge usually occurs near 90 degrees or 270 degrees, so that the phenomenon that data points are gathered near a certain phase can be obviously found out from a PRPD image during the local discharge, the local discharge point and the background noise data points can be better distinguished by adopting a density-based DBSCAN algorithm, and the quantity of TEV pulses can be better counted by further analysis processing on the basis.
Disclosure of Invention
The invention aims to provide a TEV pulse sampling and counting method for solving the defects in the prior art.
In order to achieve the above purpose, the invention provides a TEV pulse sampling counting method, which comprises the following steps:
Step 1, acquiring a TEV signal under an undersampling condition, wherein the sampling rate is set to be 250K;
step 2, downsampling the acquired TEV data to 5000 data points;
Step 3, clustering data points in the PRPD drawing drawn by the TEV data through a DBSCAN algorithm based on the PRPD drawing drawn by the TEV data, setting parameters to enable the data points to be clustered into two types, wherein one type is background noise, the other type is potential partial discharge point, removing the background noise data points, and reserving the potential partial discharge point;
Step 4, windowing, counting the number of data points meeting the requirement along the phase sliding, and screening the data points in each window according to the number of the data points in the window and the dimension integrated analysis of the amplitude;
step 5, collecting, screening and obtaining each intra-window data point image, performing rule similarity analysis on each intra-window data point, and correlating and removing intra-window interference points to finally obtain the intra-window data point of the real partial discharge point;
And 6, adding and summing the number of data points in the window for obtaining the real partial discharge point to obtain the number of final TEV pulses for output.
As a further description of the above technical solution, downsampling the acquired TEV data down to 5000 data points is specifically:
Taking the acquired TEV signals as processing objects by using data with the duration of 1 s;
the data is reconstructed into a matrix form according to the period and the phase, and then the original large matrix is downsampled to the small matrix in a mode of taking the maximum value at equal intervals along the phase.
As a further description of the above technical solution, windowing, and satisfying the required number of data points in the sliding statistical window along the phase is specifically:
windowing along the x-axis with a step length of 1, wherein the window length is less than or equal to 1/4 period;
Setting amplitude threshold values of the data sets, counting the number of data points meeting an amplitude threshold value window, and marking the data points as 、、....WhereinRepresenting the number of data points within the nth window that meet the magnitude threshold.
As a further description of the above technical solution, the screening of the data points in each window according to the integrated analysis of the number of the data points in the window and the amplitude dimension specifically includes the following steps:
calculating a threshold difference value of the ratio of the number of data points meeting the amplitude threshold value to the total amount in each window ;
Calculating the mean value difference of the stability coefficients of the data point amplitude values in each window;
For threshold differenceDifference from the mean valueAnd carrying out weighted summation to calculate the accuracy coefficient of the data point in each window, and screening the data point in each window based on the accuracy coefficient judgment.
As a further description of the above solution, a threshold difference value is calculated for the ratio of the number of data points satisfying the amplitude threshold to the total amount within each windowThe method comprises the following steps:
Setting a proportional threshold ;
Calculating the ratio of the number of data points meeting the amplitude threshold value to the total amount in each window:;
Calculating the ratio of the number of data points meeting the amplitude threshold value to the total amount in each windowSum ratio thresholdIs the difference of (2),WhereinIn order to set the value of the preset value,=0.3。
As a further description of the above-described aspects, the mean difference of the stability coefficients of the magnitudes of the data points within each window is calculatedThe method comprises the following steps:
Calculating amplitude data corresponding to the data points meeting the amplitude threshold value in each window, and integrating to obtain an amplitude data set of the data points in each window, wherein the amplitude data set of the data points in the nth window is ,∈() Amplitude data representing an mth data point within an nth window;
Calculating stability factors for the magnitudes of the data points within each window based on the magnitude dataset of the data points within each window and labeled as 、、...And integrally calculate the mean value of the stability coefficient of the data point amplitude in each window;
Calculating the difference between the stable coefficient and the mean value of the data point amplitude in the windowWherein, the method comprises the steps of, wherein,。
Further describing the technical scheme, the threshold value difference valueDifference from the mean valueThe weighted summation is carried out to calculate the accurate coefficient of the data point in each window, and the screening of the data point in each window based on the accurate coefficient judgment is specifically as follows:
Setting an accuracy coefficient judgment threshold ;
Based on the threshold differenceDifference from the mean valueCalculating the accuracy coefficient of the data point in the corresponding windowAccuracy coefficientThe calculation formula of (2) is as follows: Which is provided with 、As the weight factor of the weight factor,、Are all greater than 0;
Comparison of And (3) withIs obtained by sievingCorresponding data points in each window, andCorresponding data points within each window.
As a further description of the above technical solution: collecting, screening and obtaining each intra-window data point image, and performing rule similarity analysis and removal processing on interference points in each intra-window data point to finally obtain the intra-window data point of the real partial discharge point, wherein the method specifically comprises the following steps:
Acquisition of Corresponding data points in each window are integrated and analyzed to obtain a corrected image;
Acquisition of Corresponding data points in each window are corrected by correcting the image pairsAnd correcting and adjusting the corresponding data points in each window to finally obtain the data points in the window of the real partial discharge point.
As a further description of the above technical solution, the acquisitionThe corresponding data points in each window and the integration analysis are specifically as follows:
Calling and fetching Drawing the data points in each window in the same coordinate system by corresponding data point images in each window;
collecting the overlapping times of data points in each window, collecting the data points with the overlapping times being more than 0.75X, and keeping the collected data points in a coordinate system to obtain a corrected image, wherein X represents that the data points satisfy the requirement Number of windows.
As a further description of the above technical solution, the acquisitionCorresponding data points in each window are corrected by correcting the image pairsThe corresponding data points in each window are corrected and adjusted, and the data points in the windows for finally obtaining the real partial discharge point are specifically calledAnd drawing the corresponding data point images in each window on the corrected image, removing the data points of which the data points in the window are overlapped with the data points on the corrected image, and obtaining the data points in the window of the real partial discharge point.
According to the technical scheme, the TEV pulse sampling counting method has the advantages that the TEV pulse sampling counting method based on the DBSCAN algorithm under-sampling condition solves the problem of counting the quantity of TEV pulses under the under-sampling condition, has important significance for miniaturization and practicality of equipment based on a TEV detection mode, calculates the threshold difference value of the ratio of the quantity of data points meeting the amplitude threshold value in each window to the total quantity, calculates the mean value difference value of the stability coefficient of the amplitude of the data points in each window, calculates the accuracy coefficient of the data points in each window by carrying out weighted summation on the threshold difference value and the mean value difference value to screen the data points in each window, realizes the correlation and integration analysis of the quantity of the data points in each window and two dimensions of the amplitude of each data point, remarkably improves the accuracy and the comprehensiveness of interference point removal, and can count the quantity of the TEV pulses better.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments described in the present application, and other drawings may be obtained according to these drawings for a person having ordinary skill in the art.
FIG. 1 is a schematic flow chart of a TEV pulse sampling counting method according to an embodiment of the invention;
FIG. 2 is a PRPD pattern drawn from downsampled TEV data provided in an embodiment of the present invention;
FIG. 3 is a schematic diagram of DBSCAN clustering results provided by an embodiment of the present invention;
Fig. 4 is a schematic diagram of the number of data points in the windowed statistical window after removing the background noise according to an embodiment of the present invention.
Detailed Description
In order to make the technical scheme of the present invention better understood by those skilled in the art, the present invention will be further described in detail with reference to the accompanying drawings.
Referring to FIGS. 1-4, an embodiment of the present invention provides a TEV pulse sampling counting method, which includes the following steps:
Step 1, acquiring a TEV signal under an undersampling condition, wherein the sampling rate is set to be 250K;
Step 2, down-sampling the acquired TEV data to 5000 data points, namely, reconstructing the acquired TEV signal by taking the data with the duration of 1s as a processing object, reconstructing the data into a matrix form according to a period and a phase, then down-sampling an original large matrix into a small matrix in a mode of taking the maximum value at equal intervals along the phase, wherein the sampling rate is 250K, the original large matrix is 50 x 5000, the target matrix is 50 x 100, and the maximum value is taken at intervals of 100;
Step 3, as shown in fig. 2, clustering data points in the PRPD graph drawn based on the TEV data by a DBSCAN algorithm, as shown in fig. 3, where the well-known DBSCAN is a spatial clustering algorithm based on density, dividing a region with a high enough density into clusters, setting appropriate eps and minPts parameters according to characteristics of the TEV data, setting eps to be able to distinguish neighborhood radii of different TEV data, and determining minPts according to density distribution of the TEV data, setting parameters to make the parameters to be gathered into two types, namely, one type is bottom noise, the other type is potential partial discharge point, removing the data points of the bottom noise, and reserving the potential partial discharge point. Specifically, data points in a PRPD image are gathered into two types of background noise and potential partial discharge points, the PRPD image is firstly observed to know the distribution characteristics of the background noise and the potential partial discharge points, and preliminary clustering is carried out, wherein a relatively large eps and a moderate MinPts are selected for preliminary clustering, clustering results are observed, and parameters are adjusted, namely the eps and the MinPts are gradually adjusted according to the results of the preliminary clustering until the background noise and the potential partial discharge points can be clearly distinguished;
Step 4, windowing, namely, windowing along an x-axis with a step length of 1, wherein the window length is less than or equal to 1/4 period, setting an amplitude threshold of a data set, counting the number of data points in the window meeting the amplitude threshold, and marking the number as respectively 、、....WhereinThe data point number meeting the amplitude threshold value in the nth window is represented, and the data points in each window are screened according to the data point number in the window and the amplitude dimension integration analysis;
step 5, collecting, screening and acquiring each intra-window data point image, performing regular similarity analysis on each intra-window data point, and correlating to remove intra-window interference points, so that the intra-window data point of the real partial discharge point is finally obtained, the interference points generated by the interference sources do not have obvious phase concentration, the distribution range is wider and extends over the whole period, and each intra-window interference point corresponding to the partial discharge point is removed by performing the regular similarity analysis on each intra-window data point screened as the interference point;
And 6, adding and summing the number of data points in the window for obtaining the real partial discharge point to obtain the number of final TEV pulses for output.
The TEV pulse sampling counting method is based on the DBSCAN algorithm under the undersampling condition, solves the problem of how to realize statistics on the number of the TEV pulses under the undersampling condition, and has important significance for the miniaturization and practicability of equipment based on a TEV detection mode.
Further, the screening of the data points in each window according to the number of the data points in the window and the dimension integration analysis of the amplitude comprises the following steps:
calculating a threshold difference value of the ratio of the number of data points meeting the amplitude threshold value to the total amount in each window ;
The method comprises the following steps:
Setting a proportional threshold ;
Calculating the ratio of the number of data points meeting the amplitude threshold value to the total amount in each window:;
Calculating the ratio of the number of data points meeting the amplitude threshold value to the total amount in each windowSum ratio thresholdIs the difference of (2),WhereinIn order to set the value of the preset value,=0.3。
Calculating the mean value difference of the stability coefficients of the data point amplitude values in each windowThe method specifically comprises the following steps:
Calculating amplitude data corresponding to the data points meeting the amplitude threshold value in each window, and integrating to obtain an amplitude data set of the data points in each window, wherein the amplitude data set of the data points in the nth window is ,∈(),Amplitude data representing an mth data point within an nth window;
Calculating stability factors for the magnitudes of the data points within each window based on the magnitude dataset of the data points within each window and labeled as 、、...And integrally calculate the mean value of the stability coefficient of the data point amplitude in each window;
Calculating the difference between the stable coefficient and the mean value of the data point amplitude in the windowWherein, the method comprises the steps of, wherein,。
For threshold differenceDifference from the mean valueThe weighted summation is carried out to calculate the accurate coefficient of the data point in each window, and the data point in each window is screened based on the accurate coefficient judgment, specifically:
Setting an accuracy coefficient judgment threshold ;
Based on the threshold differenceDifference from the mean valueCalculating the accuracy coefficient of the data point in the corresponding windowAccuracy coefficientThe calculation formula of (2) is as follows: Which is provided with 、As the weight factor of the weight factor,、The size of the weight coefficient is a specific numerical value obtained by quantizing each data, so that the subsequent comparison is convenient, and the size of the weight coefficient depends on the number of the comprehensive parameters and the corresponding weight coefficient is preliminarily set for each group of comprehensive parameters by a person skilled in the art.
Comparison ofAnd (3) withIs obtained by sievingCorresponding data points in each window, andCorresponding data points in each window, whereinThe corresponding intra-window data points are potential partial discharge points including interference points,The corresponding data points within each window are interference points.
The TEV pulse sampling counting method calculates the threshold difference value of the ratio of the number of data points meeting the amplitude threshold value to the total amount in each window, calculates the average value difference value of the stability coefficient of the amplitude of the data points in each window, and calculates the accuracy coefficient of the data points in each window by carrying out weighted summation on the threshold difference value and the average value difference value so as to screen the data points in each window, thereby realizing the correlation integration analysis of the number of the data points in each window and the amplitude of each data point in two dimensions, obviously improving the accuracy and the comprehensiveness of removing the interference points and counting the TEV pulse number better.
Furthermore, collecting, screening and obtaining each intra-window data point image, and performing rule similarity analysis and removal processing on interference points in each intra-window data point to finally obtain the intra-window data point of the real partial discharge point, wherein the method specifically comprises the following steps:
Acquisition of The corresponding data points in each window are integrated and analyzed to obtain a corrected image, specifically:
Calling and fetching Drawing the data points in each window in the same coordinate system by corresponding data point images in each window;
collecting the overlapping times of data points in each window, collecting the data points with the overlapping times being more than 0.75X, and keeping the collected data points in a coordinate system to obtain a corrected image, wherein X represents that the data points satisfy the requirement Number of windows.
Acquisition ofCorresponding data points in each window are corrected by correcting the image pairsThe corresponding data points in each window are corrected and adjusted to finally obtain the data points in the window of the real partial discharge point, in particular to the callingDrawing the data points in each window on the corrected image, removing the data points in the window, which are overlapped with the data points on the corrected image, and obtaining the data points in the window of the real partial discharge point.
The TEV pulse sampling counting method is realized by acquisitionCorresponding data points in each window are integrated and analyzed to obtain a corrected image, and the corrected image pair is used for obtaining a corrected imageAnd (3) correcting and adjusting the corresponding data points in each window to finally obtain the data points in the windows of the real partial discharge points, so that the rule similarity analysis and removal processing of the interference points in the data points in each window is realized, and the accuracy of counting the number of TEV pulses is further improved.
While certain exemplary embodiments of the present invention have been described above by way of illustration only, it will be apparent to those of ordinary skill in the art that modifications may be made to the described embodiments in various different ways without departing from the spirit and scope of the invention. Accordingly, the drawings and description are to be regarded as illustrative in nature and not as restrictive of the scope of the invention, which is defined by the appended claims.

Claims (2)

1.一种TEV脉冲采样计数方法，其特征在于，包括以下步骤：1. A TEV pulse sampling and counting method, characterized in that it comprises the following steps: 步骤1：在欠采样条件下采集TEV信号，采样率设为250K；Step 1: Collect TEV signal under undersampling condition, and set the sampling rate to 250K; 步骤2：对采集的TEV数据进行降采样，降至5000个数据点；Step 2: Downsample the collected TEV data to 5000 data points; 步骤3：基于TEV数据绘制的PRPD图，通过DBSCAN算法对TEV数据绘制的PRPD图中的数据点进行聚类，设置参数使之聚为两类：一类为底噪，另一类为潜在局放点，剔除底噪的数据点，保留潜在局放点；Step 3: Based on the PRPD diagram drawn by TEV data, the data points in the PRPD diagram drawn by TEV data are clustered by using the DBSCAN algorithm, and the parameters are set to cluster them into two categories: one is the background noise, and the other is the potential partial discharge point. The data points of the background noise are eliminated, and the potential partial discharge points are retained; 步骤4：加窗，沿相位滑动统计窗内满足要求数据点数量，根据窗内数据点数量及幅值维度整合分析对各窗内数据点进行筛选，其中沿相位滑动统计窗内满足要求数据点数量具体为：以步长为1沿x轴加窗，其中窗长小于等于1/4个周期；设置数据集的幅值阈值，统计满足幅值阈值窗内数据点的数量，并分别标记为、、....，其中表示第n个窗内满足幅值阈值的数据点数量；Step 4: Add window, count the number of data points that meet the requirements in the sliding window along the phase, and screen the data points in each window according to the number of data points in the window and the amplitude dimension integration analysis. The specific number of data points that meet the requirements in the sliding window along the phase is: add window along the x-axis with a step size of 1, where the window length is less than or equal to 1/4 cycle; set the amplitude threshold of the data set, count the number of data points in the window that meet the amplitude threshold, and mark them as , , .... ,in Indicates the number of data points that meet the amplitude threshold in the nth window; 步骤5，采集筛选获取的各窗内数据点图像，对各窗内数据点进行规律相似性分析并关联去除窗内干扰点，最终获得真实局放点的窗内数据点；Step 5, collecting and screening the images of the data points in each window, performing regular similarity analysis on the data points in each window and removing the interference points in the window by association, and finally obtaining the data points in the window of the real partial discharge point; 其中根据窗内数据点数量及幅值维度整合分析对各窗内数据点进行筛选具体包括以下步骤：The screening of data points in each window according to the number of data points in the window and the integrated analysis of the amplitude dimension specifically includes the following steps: 计算各个窗内满足幅值阈值的数据点数量与总量比值的阈值差值；Calculate the threshold difference between the number of data points that meet the amplitude threshold and the total amount in each window ; 计算各窗内数据点幅值的稳定系数的均值差值；Calculate the mean difference of the stability coefficient of the amplitude of the data points in each window ; 对阈值差值与均值差值进行加权求和计算各个窗内数据点的准确系数，并基于准确系数判断对各窗内数据点进行筛选；Threshold Difference Difference from mean Perform weighted summation to calculate the accuracy coefficient of the data points in each window, and screen the data points in each window based on the accuracy coefficient; 计算各个窗内满足幅值阈值的数据点数量与总量比值的阈值差值具体为：Calculate the threshold difference between the number of data points that meet the amplitude threshold and the total amount in each window Specifically: 设置比例阈值；Set the ratio threshold ; 计算各个窗内满足幅值阈值的数据点数量与总量的比值：；Calculate the ratio of the number of data points that meet the amplitude threshold in each window to the total number : ; 计算各个窗内满足幅值阈值的数据点数量与总量的比值和比例阈值的差值，，其中为预设值，=0.3；Calculate the ratio of the number of data points that meet the amplitude threshold in each window to the total number and ratio threshold The difference , ,in is the default value, =0.3; 计算各窗内数据点幅值的稳定系数的均值差值具体为：Calculate the mean difference of the stability coefficient of the amplitude of the data points in each window Specifically: 计算各个窗内满足幅值阈值的数据点对应的幅值数据，整合获得各个窗内数据点的幅值数据集，其中第n个窗内数据点的幅值数据集为，∈（），表示第n个窗内第m个数据点的幅值数据；Calculate the amplitude data corresponding to the data points that meet the amplitude threshold in each window, and integrate to obtain the amplitude data set of the data points in each window, where the amplitude data set of the data point in the nth window is , ∈（ ), represents the amplitude data of the mth data point in the nth window; 基于各个窗内数据点的幅值数据集计算各个窗内数据点幅值的稳定系数并标记为、、...，并整合计算各个窗内数据点幅值的稳定系数均值；Based on the amplitude data set of each data point in the window, the stability coefficient of the amplitude of each data point in the window is calculated and marked as , , ... , and integrate to calculate the stability coefficient mean of the amplitude of each data point in the window ; 计算窗内数据点幅值的稳定系数与均值的差值，其中，；Calculate the difference between the stability coefficient of the data point amplitude in the window and the mean ,in, ; 对阈值差值与均值差值进行加权求和计算各个窗内数据点的准确系数，并基于准确系数判断对各窗内数据点进行筛选具体为：Threshold Difference Difference from mean The weighted sum is performed to calculate the accuracy coefficient of each data point in the window, and the data points in each window are screened based on the accuracy coefficient: 设置准确性系数判断阈值；Set the accuracy coefficient judgment threshold ; 基于阈值差值与均值差值计算对应窗内数据点的准确系数，准确系数的计算公式为：，其、为权重因子，、均大于0；Based on the difference threshold Difference from mean Calculate the accuracy coefficient of the corresponding data point in the window , accuracy coefficient The calculation formula is: ,That , is the weight factor, , All are greater than 0; 比较与的大小，筛分获取对应的各窗内数据点，以及对应的各窗内数据点；Compare and Size, sieving to obtain The corresponding data points in each window, and The corresponding data points in each window; 采集筛选获取的各窗内数据点图像，对各窗内数据点进行规律相似性分析并关联去除窗内干扰点，最终获得真实局放点的窗内数据点具体包括以下步骤：The images of the data points in each window are collected and screened, and the regular similarity analysis is performed on the data points in each window, and the interference points in the window are removed by association, so as to finally obtain the data points in the window of the real partial discharge point. Specifically, the following steps are included: 采集对应的各窗内数据点并整合分析获取修正图像；collection The corresponding data points in each window are integrated and analyzed to obtain a corrected image; 采集对应的各窗内数据点，通过修正图像对对应的各窗内数据点进行修正调整，最终获得真实局放点的窗内数据点；collection The corresponding data points in each window are corrected by The corresponding data points in each window are corrected and adjusted, and finally the data points in the window of the real partial discharge point are obtained; 采集对应的各窗内数据点并整合分析获取修正图像具体为：collection The corresponding data points in each window are integrated and analyzed to obtain the corrected image: 调取对应的各窗内数据点图像，将各窗内数据点绘制在同一坐标系中；采集各窗内数据点重叠次数，采集重叠次数大于0.75X的数据点，并将采集的数据点保留在坐标系中获得修正图像，其中X表示满足窗数量；Retrieve The corresponding data point images in each window are plotted in the same coordinate system; the overlap times of the data points in each window are collected, and the data points whose overlap times are greater than 0.75X are collected, and the collected data points are retained in the coordinate system to obtain a corrected image, where X represents the data points that meet Number of windows; 采集对应的各窗内数据点，通过修正图像对对应的各窗内数据点进行修正调整，最终获得真实局放点的窗内数据点具体为：调取对应的各窗内数据点图像；将各窗内数据点绘制在修正图像上，去除窗内数据点与修正图像上数据点相重合的数据点，获得真实局放点的窗内数据点；collection The corresponding data points in each window are corrected by The corresponding data points in each window are corrected and adjusted, and the data points in the window of the actual partial discharge point are finally obtained as follows: The corresponding data point images in each window; the data points in each window are plotted on the corrected image, and the data points in the window that overlap with the data points on the corrected image are removed to obtain the data points in the window of the real partial discharge point; 步骤6，将获得真实局放点的窗内数据点数量相加求和获得最终TEV脉冲的数量进行输出。Step 6, adding the number of data points in the window for obtaining the real partial discharge point to obtain the final number of TEV pulses for output. 2.根据权利要求1所述的一种TEV脉冲采样计数方法，其特征在于，对采集的TEV数据进行降采样，降至5000个数据点具体为：2. A TEV pulse sampling and counting method according to claim 1, characterized in that the collected TEV data is downsampled to 5000 data points as follows: 将采集的TEV信号以时长1s的数据为处理对象；The collected TEV signal is processed with the data of 1s duration; 按周期以及相位将数据重构成矩阵形式，接着沿相位等间隔取最大值的方式将原先的大矩阵降采样至小矩阵。The data is reconstructed into a matrix form according to the period and phase, and then the original large matrix is downsampled to a small matrix by taking the maximum value at equal intervals along the phase.
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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112084933A (en) * 2020-09-08 2020-12-15 贵州电网有限责任公司 Denoising method for partial discharge signal of transformer
CN112668612A (en) * 2020-12-09 2021-04-16 重庆邮电大学 Partial discharge signal clustering analysis method based on grids

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