Improvement of Partial Discharge Recognition Reliability Considering Influence of Multi-factors Based on Two- Directional Modified Fuzzy 2DLDA Algorithm
Wang Ke1,2,Liao Ruijin1,Wu Gaolin3,Wang Qian3,Wu Feifei1
1. State Key Laboratory of Power Transmission Equipment & System Security and New TechnologyChongqing University Chongqing 400044 China 2. China Electric Power Research Institute Beijing 100192 China 3. State Grid Chongqing Electric Power Co. Electric Power Research Institute Chongqing 401123 China
Abstract：The variation of defect size and applied voltage,and insulation aging can give rise to the dispersion and crossover of partial discharge (PD) features,which will influence the PD recognition reliability in transformers greatly. In order to solve the above problems,this paper presents a two-directional modified fuzzy 2DLDA (TD-MF2DLDA) algorithm,aiming to extract discriminatory features from PD gray images to infer the corresponding defect types. The method firstly designs a modified fuzzy 2DLDA (MF2DLDA) algorithm which is the foundation of TD-MF2DLDA for PD gray image compression. Finally,a fuzzy C-means (FCM) clustering tool is employed to carry out comparisons of different features. The recognition results of experimental PD samples in laboratory demonstrate that TD-MF2DLDA can achieve a successful clustering rate of 92.84% which is much higher than 77.80% of nonnegative matrix factorization aided principal component analysis (NMF-PCA) and 71.12% of traditional phase-resolved PD(PRPD) features. It could be found that TD-MF2DLDA can effectively eliminate the features dispersion and crossover derived from defect size,applied voltage and insulation aging,and thereby improve the PD recognition reliability. In addition,the FCM clustering validity measure Xie and Beni’s index (XBI) shows that features obtained by TD-MF2DLDA have better compactness within class and separability between classes than NMF-PCA and PRPD features,which is more suitable for on-site applications.
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