CN113343550A - Partial discharge fault diagnosis method based on local image characteristics - Google Patents

Partial discharge fault diagnosis method based on local image characteristics Download PDF

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CN113343550A
CN113343550A CN202110641395.3A CN202110641395A CN113343550A CN 113343550 A CN113343550 A CN 113343550A CN 202110641395 A CN202110641395 A CN 202110641395A CN 113343550 A CN113343550 A CN 113343550A
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李泽
钱勇
陈孝信
臧奕茗
王辉
舒博
盛戈皞
江秀臣
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Electric Power Research Institute of State Grid Zhejiang Electric Power Co Ltd
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Abstract

A partial discharge fault diagnosis method based on partial image features comprises the following steps: collecting partial discharge images of different fault types, preprocessing the images and generating a partial discharge image database; extracting local features of the partial discharge image by adopting a U-SURF-BoW algorithm, and constructing a partial discharge image feature space; and then, carrying out dimensionality reduction on the feature space by adopting PCA, then carrying out parameter optimization operation on the support vector machine by using a longicorn algorithm, constructing an optimal partial discharge support vector machine classification model, and finally, identifying and classifying the partial discharge image data features by adopting the classification model to obtain a final fault diagnosis result. The invention can effectively and stably extract the partial discharge characteristics, improve the efficiency and the accuracy of the partial discharge fault diagnosis and simultaneously ensure that the partial discharge fault diagnosis has better identification effect under the condition of high noise.

Description

Partial discharge fault diagnosis method based on local image characteristics
Technical Field
The invention belongs to the field of partial discharge mode identification, and particularly relates to a partial discharge fault diagnosis method based on partial image characteristics.
Background
Partial Discharge (PD) is an important symptom reflecting insulation degradation of electrical equipment, and different Discharge types differ in insulation degradation mechanism, Discharge development process, and harmfulness. Therefore, the type of the partial discharge is timely and effectively identified, and the method has important significance for equipment fault identification and insulation state evaluation. An ultrahigh frequency (UHF) detection method is widely applied to PD detection and pattern recognition as a detection method which has high sensitivity, can position a discharge source and has strong anti-interference capability. The principle of the uhf detection method is to monitor the occurrence of partial discharge by using high frequency electromagnetic waves excited by a pulse current generated by the partial discharge.
In order to improve the efficiency of partial discharge fault diagnosis, an intelligent algorithm needs to be used instead of manpower, and pattern recognition is a tool for intelligent diagnosis. For GIS partial discharge mode identification, the current common feature extraction method comprises statistical feature, texture feature, shape feature and other methods. However, these algorithms also have certain limitations, are not good for recognition in a noisy field environment, and are easy to extract a large number of redundant features. Currently, the classifiers mainly include back propagation neural networks (called BPNN), KNN algorithms, etc., however, the structure of BPNN is difficult to determine, and the parameters to be adjusted are many. The KNN principle is simple, and the method is well applied to the field of partial discharge mode identification, but the algorithm is low in efficiency when new data are tested.
Disclosure of Invention
The invention aims to overcome the defects in the prior art, and provides a partial discharge fault diagnosis method based on local image characteristics so as to obtain a better partial discharge fault type classification effect and further realize automatic diagnosis and accurate identification of partial discharge faults.
In order to achieve the purpose, the invention adopts the following technical scheme:
a partial discharge fault diagnosis method based on partial image features is characterized by comprising the following steps:
1) respectively acquiring ultrahigh frequency signal images of partial discharge of A fault types by using ultrahigh frequency sensors to serve as a sample set X ═ X1,X2,…,Xi,…,XA}，1<i≤A，XiA set of samples representing signals of the i-th class; and is
Figure BDA0003107941480000021
n represents the total number of partial discharge signal samples for the i-th fault,
Figure BDA0003107941480000022
j is more than or equal to 1 and less than or equal to N, and the total number of partial discharge ultrahigh frequency signal image samples is N;
2) using a document [1 ]]The method calculates the BoW feature packet of all partial discharge ultrahigh frequency signal image features (see the literature [1 ]]Sivic, Josef. effective visual search of video cassette as a text retrieval. IEEE Transactions on pattern analysis and machine interpretation, vol.31, No.4.IEEE: 591-605. April 2009) to extract the U-SURF characteristics of all partial discharge ultrahigh frequency signal image sample sets X to obtain all sample sets XSet of features B ═ B1,B2,…,Bi,…,BA}. Wherein, BiA feature set representing the signal of the i-th class; and is
Figure BDA0003107941480000023
Figure BDA0003107941480000024
And representing the characteristics of the jth sample in the ith fault, wherein the characteristics of each sample are 1 × H-dimensional vectors, and a corresponding fault type label is added to the characteristic set of the A fault types and is marked as Y ═ Y1,Y2,…,Yi,…,YA}; using a document [1 ]]Calculating BoW feature packets of all partial discharge ultrahigh frequency signal image features to obtain a visual word frequency feature set C ═ C1,C2,…,Ci,…,CA}，CiA feature set representing the signal of the i-th class; and is
Figure BDA0003107941480000025
Figure BDA0003107941480000026
Representing the characteristics of the jth sample in the ith type of fault, wherein the characteristics of each sample are vectors with the dimensions of 1 x L;
2.1) converting the sample set X into a gray scale graph G, removing numerical information of coordinates, controlling the pixel size of each picture to be consistent, wherein the pixel size is r, the expression of any pixel point in the picture is D (a, b), and the value ranges of a and b are as follows: a is more than or equal to 1 and less than or equal to r, and b is more than or equal to 1 and less than or equal to r;
2.2) for each gray-scale image GiDetermining the U-SURF characteristic points of the image according to the following method:
extracting a Hessian matrix with the scale of sigma at the point D in the image:
Figure BDA0003107941480000027
wherein L isxx(D,σ)、Lxy(D,σ)、Lyy(D, σ) represents the convolution of the gaussian second-order partial derivative with the image at D;
the convolution of the image and the second derivative is respectively expressed by Dxx, Dxy and Dyy approximation, and the approximate determinant value of the Hessian matrix is obtained according to the following formula:
det(Happroximate)＝DxxDyy-(wDxy)2
then adopting a non-maximum suppression principle in a neighboring area with the size of 3 multiplied by 3, selecting an image where the maximum value of the Hessian matrix determinant is located and the scale space position thereof as characteristic points, and calling the characteristic points as the gray image GiRepeating the step 2.2) to obtain all gray level images GiThe U-SURF feature points of (1);
2.3) calculating each grayscale image GiThe description vector of SURF feature points of (1):
taking the U-SURF feature point as a center, selecting a 20s × 20s square area, where s represents the scale of the feature point, and dividing the area into 4 × 4 sub-areas with the best effect, then calculating a corresponding 4-dimensional feature vector V ═ Σ dx, Σ | dx |, Σ dy, Σ | dy | } in each sub-area, and forming a 4 × 4 × 4 ═ 64-dimensional description vector at each feature point;
2.4) the features obtained in step 2.3) are all mapped to the vocabulary of the visual dictionary by calculating the distance between the features according to the following method:
constructing a word list by using a K-means algorithm, dividing all characteristics into L clusters by taking K as a parameter, enabling the clusters to have higher similarity and lower inter-cluster similarity, regarding each cluster center as a visual vocabulary in a dictionary, obtaining L visual vocabularies in total, and enabling all the visual vocabularies to form a visual dictionary;
2.5) counting the occurrence frequency and the occurrence frequency of each visual word in each picture to obtain the frequency of the visual words with different partial discharge image characteristics;
3) calculating BoW feature packets of all partial discharge ultrahigh frequency signal image features to obtain a visual word frequency feature set C ═ C of all sample sets1,C2,…,Ci,…,CA}，CiA feature set representing the signal of the i-th class; and is
Figure BDA0003107941480000031
Figure BDA0003107941480000032
Representing the characteristics of the jth sample in the ith type of fault, wherein the characteristics of each sample are vectors with the dimensions of 1 x L;
4) after the acquired U-SURF characteristics are subjected to dimensionality reduction by using the following PCA dimensionality reduction method, a dimensionality reduction characteristic matrix P is obtained:
4.1) zero-averaging each column of the U-SURF feature, i.e. subtracting the average of this row;
4.2) solving the covariance matrix and the eigenvalue and eigenvector of the covariance matrix;
4.3) arranging the eigenvectors into a matrix from top to bottom according to the sizes of the corresponding eigenvalues, taking the data of the first k columns to form a dimension-reduced eigenvector matrix P, wherein P is { P ═ P { (P)1,P2,…,Pi,…,PA},PiA feature set representing the signal of the i-th class; and is
Figure BDA0003107941480000033
Figure BDA0003107941480000034
Features representing the q-th sample in a class i fault, where 1<q≤k＜L；
5) Normalizing the dimension-reduced feature matrix P to obtain a defect feature set U ═ U1,U2,…,Ui,…,UZ}，UiThe method is characterized in that an ith defect characteristic sample corresponding to a GIS partial discharge ultrahigh frequency signal is represented, and the method comprises the following steps:
Figure BDA0003107941480000041
Figure BDA0003107941480000042
representing the jth sample in the ith type defect characteristic sample; and the jth sample
Figure BDA0003107941480000043
K normalized statistical characteristics are included, i is more than or equal to 1 and less than or equal to A, j is more than or equal to 1 and less than or equal to M, and M represents the total number of the ith defect characteristic samples;
6) initializing the longicorn tentacle length, the longicorn motion step length, the longicorn iteration times and the longicorn tentacle position, and constructing an initial support vector machine model:
6.1) initializing the antenna whisker length to be s, the antenna movement step length to be u and the antenna iteration number to be tmaxThe two-dimensional position coordinate vector of the two tentacles of the longicorn is P0＝{PL,PRIn which P isLRepresenting the coordinates of the longicorn left-tentacle position, PRRepresenting the coordinates of the right tentacle of the longicorn;
using said two-dimensional position coordinate vector P0Initializing parameters of a support vector machine by coordinate values in the x direction and the y direction of the two tentacles, wherein the coordinate value in the x direction represents a parameter c of the support vector machine, and the coordinate value in the y direction represents a parameter g of the support vector machine;
the support vector machine finds a partition hyperplane in a sample space based on a training set, and separates samples of different classes. The support vector machine can be converted to solve the convex quadratic programming problem:
Figure BDA0003107941480000044
yi(ωTxi+b)≥1-ξi,i＝1,2,...,N
ξi≥0,i＝1,2,...,N
wherein: omega is hyperplane normal vector, b is offset, C is penalty factor, xiiIs a relaxation factor;
introducing a kernel function k (x)iX), then the final decision equation for the classifier is:
Figure BDA0003107941480000045
wherein: alpha is alphai *Is the optimal Lagrangian multiplier, b*Is the optimum offset;
6.2) defining the current iteration number as t, and initializing t to be 1;
two-dimensional coordinate vector P of longicorn beard0Two-dimensional coordinate vector P as the t-th iterationt(ii) a Taking the initial support vector machine model as a support vector machine model of the t iteration;
6.3) two-dimensional coordinate vector P with the t-th iterationtConstructing a support vector machine model of the t iteration, performing cross validation on the e defect feature sample subsets by using the support vector machine model of the t iteration to obtain a partial discharge defect error rate, and taking the partial discharge defect error rate as a fitness value of the t iteration in the longicorn algorithm;
6.4) selecting a smaller value in the fitness values of the t iteration corresponding to the left and right longicorn whiskers, taking the smaller value as a local optimal value of the t iteration, and taking the tentacle coordinate of the longicorn corresponding to the local optimal value;
6.5) the longicorn moves to the tentacle side corresponding to the local optimal value of the t iteration according to the motion step u of the longicorn, thereby obtaining a two-dimensional coordinate vector P of the t +1 iterationt+1；
6.6) assigning t +1 to t, and then judging whether t reaches tmaxIf not, returning to step 6.3), if yes, selecting tmaxTaking the minimum value in the local optimal values of the secondary iteration as a global optimal value; constructing a support vector machine model for diagnosing the partial discharge fault by taking the two-dimensional coordinate vector corresponding to the global optimal value as an optimal support vector machine parameter; and go to the next step;
7) and diagnosing the test sample set of the partial discharge ultrahigh frequency signal image by using the support vector machine model for partial discharge fault diagnosis, and obtaining a final classification result so as to output a fault diagnosis result of partial discharge.
Compared with the prior art, the invention has the beneficial effects that:
1. the method for extracting the partial discharge characteristics based on the U-SURF-BoW solves the problems that the traditional method is difficult to apply to a noisy environment and extracts a large number of redundant characteristics, and meanwhile, the PCA method is adopted to perform dimension reduction processing on the extracted characteristics, so that the problems of characteristic redundancy and large calculated amount in the model are solved. The method improves the effectiveness and the recognition efficiency of the feature extraction of the partial discharge image, and simultaneously ensures the accuracy of the partial discharge fault diagnosis.
2. The invention introduces the longicorn whisker algorithm into the support vector machine, and performs characteristic selection and parameter optimization on the support vector machine through the longicorn whisker algorithm. Compared with the traditional method for optimizing the support vector machine by using the intelligent algorithm, the method can more efficiently and accurately finish the diagnosis and identification of the partial discharge fault, and improves the classification precision and efficiency of the support vector machine.
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FIG. 1 is a schematic flow diagram of the present invention;
Detailed Description
In this embodiment, a partial discharge fault diagnosis method based on local image features, as shown in fig. 1, includes the following steps:
1) respectively acquiring partial discharge ultrahigh frequency signal images of A fault types by using ultrahigh frequency sensors as a sample set X ═ X1,X2,…,Xi,…,XA}，1<i≤A，XiA set of samples representing signals of the i-th class; and is
Figure BDA0003107941480000051
n represents the total number of samples of the ith type fault partial discharge signal,
Figure BDA0003107941480000052
j is more than or equal to 1 and less than or equal to N, and the total number of partial discharge ultrahigh frequency signal image samples is N; the types of the partial discharge defects are generally divided into 4 types, namely metal tip defects, suspended electrode defects, free metal particle defects and air gap model defects;
2) extracting U-SURF characteristics of all partial discharge ultrahigh frequency signal image sample sets X to obtain all partial discharge ultrahigh frequency signal image sample sets XCharacteristic set B ═ B of sample set1,B2,…,Bi,…,BA}，BiA feature set representing the signal of the i-th class; and is
Figure BDA0003107941480000061
Figure BDA0003107941480000062
And representing the characteristics of the jth sample in the ith fault, wherein the characteristics of each sample are 1 × H-dimensional vectors, and a corresponding fault type label is added to the characteristic set of the A fault types and is marked as Y ═ Y1,Y2,…,Yi,…,YA}; using a document [1 ]]Calculating BoW feature packets of all partial discharge ultrahigh frequency signal image features to obtain a visual word frequency feature set C ═ C of all sample sets1,C2,…,Ci,…,CA}，CiA feature set representing the signal of the i-th class; and is
Figure BDA0003107941480000063
Figure BDA0003107941480000064
And representing the characteristics of the jth sample in the ith fault, wherein the characteristics of each sample are vectors with the dimensions of 1 x L.
2.1) converting the partial discharge ultrahigh frequency signal image X into a gray level image G, thereby achieving the purpose of improving the calculation efficiency; removing the numerical information of the coordinates to avoid the interference of the numerical information on the identification process; the pixel sizes of all the pictures are controlled to be consistent and are r multiplied by r, wherein D (a, b) is any pixel point in the pictures, a is larger than or equal to 1 and smaller than or equal to r, and b is larger than or equal to 1 and smaller than or equal to r.
2.2) for each gray-scale GiAnd determining U-SURF characteristic points of the image.
Extracting a Hessian matrix with the scale of sigma at the point D in the image:
Figure BDA0003107941480000065
wherein L isxx(D,σ)、Lxy(D,σ)、Lyy(D, σ) represents the convolution of the gaussian second-order partial derivative with the image at D;
in order to improve the calculation efficiency, the convolution of the image and the second derivative is respectively approximately represented by Dxx, Dxy and Dyy, and the approximate determinant value of the Hessian matrix is obtained:
det(Happroximate)＝DxxDyy-(wDxy)2
then adopting a non-maximum suppression principle in a neighboring region with the size of 3 multiplied by 3, and selecting an image where the maximum value of the Hessian matrix determinant is located and the scale space position of the image as feature points, namely SURF feature points;
2.3) calculating each grayscale image GiThe description vector of SURF feature points of (1):
and selecting a 20s × 20s square area by taking the U-SURF feature point as a center, and dividing the area into 4 × 4 sub-areas. Then, the corresponding 4-dimensional feature vector V ═ Σ dx, Σ | dx |, Σ dy, Σ | dy | } in each sub-region is calculated, respectively. A description vector of 4 × 4 × 4 ═ 64 dimensions is formed at each feature point.
And 2.4, mapping all the characteristics obtained in the step 2.3 into the vocabulary of the visual dictionary by calculating the distance between the characteristics. And constructing a word list by using a K-means algorithm. And taking K as a parameter, and dividing all the characteristics into L clusters, so that the clusters have higher similarity and the inter-cluster similarity is lower. Each cluster center is considered to be a visual vocabulary in the dictionary. Obtaining L visual vocabularies in total, wherein all the visual vocabularies form a visual dictionary;
and 2.5, counting whether each visual word appears or not and the appearance frequency of each visual word in each picture to obtain the visual word frequency of different partial discharge image characteristics.
And 3, performing dimensionality reduction on the acquired U-SURF characteristics by using a PCA dimensionality reduction method.
Step 3.1, carrying out zero equalization on each column of the U-SURF characteristics, namely subtracting the average value of the row;
step 3.2, solving the covariance matrix and the eigenvalue and eigenvector of the covariance matrix
Step 3.3, arranging the eigenvectors into a matrix from top to bottom according to the sizes of the corresponding eigenvalues, and taking the data of the first k columns to form a matrix P, wherein P is { P ═ P1,P2,…,Pi,…,PA},PiA feature set representing the signal of the i-th class; and is
Figure BDA0003107941480000071
Figure BDA0003107941480000072
Features representing the q-th sample in a class i fault, where 1<q≤k＜L。
Step 4, the extracted feature matrix is subjected to normalization processing, so that the speed and the efficiency of subsequent pattern recognition are improved conveniently, and a defect feature set U is obtained1,U2,…,Ui,…,UZ}，UiThe method is characterized in that a sample of the ith defect characteristics corresponding to the partial discharge ultrahigh frequency signal is represented, and the sample comprises the following components:
Figure BDA0003107941480000073
Figure BDA0003107941480000074
representing the jth sample in the ith type defect characteristic sample; and the jth sample
Figure BDA0003107941480000075
K normalized statistical characteristics are included, i is more than or equal to 1 and less than or equal to A, j is more than or equal to 1 and less than or equal to M, and M represents the total number of the ith defect characteristic samples;
step 5, initializing the longicorn tentacle length, the longicorn motion step length, the longicorn iteration times and the longicorn tentacle position, and constructing an initial support vector machine model;
step 5.1, initializing the antenna whisker length to be s, the antenna motion step length to be u and the antenna iteration number to be tmaxThe two-dimensional position coordinate vector of the two tentacles of the longicorn is P0＝{PL,PRIn which P isLRepresenting the coordinates of the longicorn left-tentacle position, PRRepresenting the coordinates of the right tentacle of the longicorn;
using said two-dimensional position coordinate vector P0Initializing parameters of a support vector machine by coordinate values in the x direction and the y direction of the two tentacles, wherein the coordinate value in the x direction represents a parameter c of the support vector machine, and the coordinate value in the y direction represents a parameter g of the support vector machine;
step 5.2, defining the current iteration number as t, and initializing t to be 1;
two-dimensional coordinate vector P of longicorn beard0Two-dimensional coordinate vector P as the t-th iterationt(ii) a Taking the initial support vector machine model as a support vector machine model of the t iteration;
step 5.3, utilizing the t-th iteration two-dimensional coordinate vector PtConstructing a support vector machine model of the t iteration, performing cross validation on the e defect feature sample subsets by using the support vector machine model of the t iteration to obtain the partial discharge defect error rate of the support vector machine model of the t iteration, and taking the partial discharge defect error rate as the fitness value of the t iteration in the Tianniu whisker algorithm;
step 5.4, selecting a smaller value of the fitness values of the t iteration corresponding to the left and right longicorn whiskers, taking the smaller value as a local optimal value of the t iteration, and obtaining the tentacle coordinates of the longicorn corresponding to the local optimal value;
step 5.5, the longicorn moves to one side of the tentacle corresponding to the local optimal value of the t iteration according to the motion step u of the longicorn, and therefore the two-dimensional coordinate vector P of the t +1 iteration is obtainedt+1；
Step 5.6, after assigning t +1 to t, judging whether t reaches tmaxIf yes, then select tmaxTaking the minimum value in the local optimal values of the secondary iteration as a global optimal value; taking the two-dimensional coordinate vector corresponding to the global optimal value as an optimal support vector machine parameter, thereby constructing a support vector machine model for diagnosing the partial discharge fault; otherwise, returning to the step 6.3;
and 6, diagnosing the test sample set of the partial discharge ultrahigh frequency signal image by using the support vector machine model for partial discharge fault diagnosis, and outputting a fault classification result of partial discharge.
In order to verify the accuracy of the method, a support vector machine (BAS-SVM) model based on the longicorn whisker algorithm and basic SVM and BPNN are simultaneously established for comparison.
The method comprises the steps of acquiring 400 groups of GIS insulation defect partial discharge data in an ultrahigh frequency partial discharge experiment, dividing samples into training samples and testing samples, randomly selecting 300 groups of training samples, and testing the rest testing samples to test a trained classifier.
The partial discharge fault diagnosis is realized by respectively adopting 3 different classifiers of SVM (support vector machine) based on Particle Swarm Optimization (PSO), SVM based on celestial cow whisker optimization (BAS) and BPNN. The U-SURF-BoW features are put into a classifier for learning, then the trained classifier is used for classifying and identifying the test samples, and the obtained identification result is shown in the following table:
TABLE 1 comparison of recognition effects of different algorithms
Figure BDA0003107941480000081
As is apparent from comparing the values in table 1, the overall recognition accuracy of the BAS-SVM algorithm is the highest among the 3 classifiers, which is 98.9%, and is much higher than the accuracy of 92.6% of the basis SVM model and 89.1% of the RF model. Meanwhile, the recognition results of the BAS-SVM and the basic SVM are compared, so that the recognition accuracy of the BAS-SVM model is improved by 6.2% compared with that of the basic SVM model, and the longicorn algorithm is proved to be feasible and effective for optimizing the support vector machine. Meanwhile, the effectiveness of the U-SURF-BoW feature extraction method can be seen through the identification result. The experimental result proves that the method can accurately diagnose the type of the partial discharge fault, and simultaneously provides detection and judgment basis for operation and maintenance personnel in an actual field, thereby being beneficial to the safe and stable operation of the power grid.

Claims (1)

1. A partial discharge fault diagnosis method based on partial image features is characterized by comprising the following steps:
1) local discharge for collecting different fault typesAnd (3) electrically imaging, preprocessing the image, and generating a local discharge image database: respectively acquiring ultrahigh frequency signal images of partial discharge of A fault types by using ultrahigh frequency sensors to serve as a sample set X ═ X1,X2,…,Xi,…,XA}，1<i≤A，XiA set of samples representing signals of the i-th class; and is
Figure FDA0003107941470000011
n represents the total number of partial discharge signal samples for the i-th fault,
Figure FDA0003107941470000012
j is more than or equal to 1 and less than or equal to N, and the total number of partial discharge ultrahigh frequency signal image samples is N;
2) extracting local features of the partial discharge image by adopting a U-SURF-BoW algorithm, and constructing a partial discharge image feature space: comprises the following steps:
2.1) converting the sample set X into a gray scale graph G, removing numerical information of coordinates, controlling the pixel size of each picture to be consistent, wherein the pixel size is r, the expression of any pixel point in the picture is D (a, b), and the value ranges of a and b are as follows: a is more than or equal to 1 and less than or equal to r, and b is more than or equal to 1 and less than or equal to r;
2.2) for each gray-scale image GiDetermining the U-SURF characteristic points of the image according to the following method:
extracting a Hessian matrix with the scale of sigma at the point D in the image:
Figure FDA0003107941470000013
wherein L isxx(D,σ)、Lxy(D,σ)、Lyy(D, σ) represents the convolution of the gaussian second-order partial derivative with the image at D;
the convolution of the image and the second derivative is approximately represented by Dxx, Dxy and Dyy respectively, and the approximate determinant value of the Hessian matrix is obtained as follows:
det(Happroximate)＝DxxDyy-(wDxy)2
then adopting a non-maximum suppression principle in a neighboring area with the size of 3 multiplied by 3, selecting an image where the maximum value of the Hessian matrix determinant is located and the scale space position thereof as characteristic points, and calling the characteristic points as the gray image GiRepeating the step 2.2) to obtain all gray level images GiThe U-SURF feature points of (1);
2.3) calculating each grayscale image GiThe description vector of SURF feature points of (1):
taking the U-SURF feature point as a center, selecting a 20s × 20s square area, where s represents the scale of the feature point, dividing the area into 4 × 4 sub-areas with the best effect, and then calculating a corresponding 4-dimensional feature vector V ═ Σ dx, Σ | dx |, Σ dy, Σ | dy | } in each sub-area, so as to form a 4 × 4 × 4 ═ 64-dimensional description vector at each feature point;
2.4) the features obtained in step 2.3) are all mapped to the vocabulary of the visual dictionary by calculating the distance between the features according to the following method:
constructing a word list by using a K-means algorithm, dividing all characteristics into L clusters by taking K as a parameter, enabling the clusters to have higher similarity and lower inter-cluster similarity, regarding each cluster center as a visual vocabulary in a dictionary, obtaining L visual vocabularies in total, and enabling all the visual vocabularies to form a visual dictionary;
2.5) counting the occurrence frequency and the occurrence frequency of each visual word in each picture to obtain the frequency of the visual words with different partial discharge image characteristics;
extracting U-SURF characteristics of all partial discharge ultrahigh frequency signal image sample sets X to obtain characteristic sets B ═ B of all sample sets1,B2,…,Bi,…,BA}，BiA feature set representing the signal of the i-th class; and is
Figure FDA0003107941470000021
Figure FDA0003107941470000022
Is shown asThe characteristics of the jth sample in the i-type faults are 1 × H-dimensional vectors, corresponding fault type labels are added to the characteristic set of the A-type fault types, and the labels are recorded as Y ═ Y1,Y2,…,Yi,…,YA}；
3) Calculating BoW feature packets of all partial discharge ultrahigh frequency signal image features to obtain a visual word frequency feature set C ═ C of all sample sets1,C2,…,Ci,…,CA}，CiA feature set representing the signal of the i-th class; and is
Figure FDA0003107941470000023
Figure FDA0003107941470000024
Representing the characteristics of the jth sample in the ith type of fault, wherein the characteristics of each sample are vectors with the dimensions of 1 x L;
4) after the dimension reduction processing is carried out on the obtained U-SURF-BOW features by using a PCA dimension reduction method, obtaining a dimension reduction feature matrix P comprises the following steps:
4.1) zero-averaging each column of the U-SURF feature, i.e. subtracting the average of this row;
4.2) solving the covariance matrix and the eigenvalue and eigenvector of the covariance matrix;
4.3) arranging the eigenvectors into a matrix from top to bottom according to the sizes of the corresponding eigenvalues, taking the data of the first k columns to form a dimension-reduced eigenvector matrix P, wherein P is { P ═ P { (P)1,P2,…,Pi,…,PA},PiA feature set representing the signal of the i-th class; and P isi＝{Pi 1,Pi 2,…,Pi q,…,Pi k}，Pi qFeatures representing the q-th sample in a class i fault, where 1<q≤k＜L；
5) Normalizing the dimension-reduced feature matrix P to obtain a defect feature set U ═ U1,U2,…,Ui,…,UZ}，UiIndicating the class i defect characteristics corresponding to GIS partial discharge ultrahigh frequency signalsCharacterizing the sample, and having:
Figure FDA0003107941470000025
Figure FDA0003107941470000026
representing the jth sample in the ith type defect characteristic sample; and the jth sample
Figure FDA0003107941470000027
K normalized statistical characteristics are included, i is more than or equal to 1 and less than or equal to A, j is more than or equal to 1 and less than or equal to M, and M represents the total number of the ith defect characteristic samples;
6) initializing the longicorn tentacle length, the longicorn motion step length, the longicorn iteration times and the longicorn tentacle position, and constructing an initial support vector machine model:
6.1) initializing the antenna whisker length to be s, the antenna movement step length to be u and the antenna iteration number to be tmaxThe two-dimensional position coordinate vector of the two tentacles of the longicorn is P0＝{PL,PRIn which P isLRepresenting the coordinates of the longicorn left-tentacle position, PRRepresenting the coordinates of the right tentacle of the longicorn; using said two-dimensional position coordinate vector P0Initializing parameters of a support vector machine by coordinate values in the x direction and the y direction of the two tentacles, wherein the coordinate value in the x direction represents a parameter c of the support vector machine, and the coordinate value in the y direction represents a parameter g of the support vector machine;
6.2) defining the current iteration number as t, and initializing t to be 1 by using a two-dimensional coordinate vector P of the longicorn beard0Two-dimensional coordinate vector P as the t-th iterationt(ii) a Taking the initial support vector machine model as a support vector machine model of the t iteration;
6.3) two-dimensional coordinate vector P with the t-th iterationtConstructing a support vector machine model of the t iteration, performing cross validation on the e defect feature sample subsets by using the support vector machine model of the t iteration to obtain a partial discharge defect error rate, and taking the partial discharge defect error rate as a fitness value of the t iteration in the longicorn algorithm;
6.4) selecting a smaller value in the fitness values of the t iteration corresponding to the left and right longicorn whiskers, taking the smaller value as a local optimal value of the t iteration, and taking the tentacle coordinate of the longicorn corresponding to the local optimal value;
6.5) the longicorn moves to the tentacle side corresponding to the local optimal value of the t iteration according to the motion step u of the longicorn, thereby obtaining a two-dimensional coordinate vector P of the t +1 iterationt+1；
6.6) assigning t +1 to t, and then judging whether t reaches tmaxIf not, returning to step 6.3), if yes, selecting tmaxTaking the minimum value in the local optimal values of the secondary iteration as a global optimal value; constructing a support vector machine model for diagnosing the partial discharge fault by taking the two-dimensional coordinate vector corresponding to the global optimal value as an optimal support vector machine parameter; and go to the next step;
7) and diagnosing the test sample set of the partial discharge ultrahigh frequency signal image by using the support vector machine model for partial discharge fault diagnosis, and outputting a fault diagnosis result of partial discharge.
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