Partial discharge characteristic optimization of cables based on support vector machine-recursive feature elimination
Li Cheng1, Li Qiang1, Zhang Qichao2, Liu Zirui3, Li Wei4
1. State Grid Hanzhong Electric Power Supply Company, Hanzhong, Shaanxi 723000; 2. Shanghai University of Electric Power, Shanghai 200090; 3. State Grid Shaanxi Electric Power Company, Xi’an 710048; 4. State Grid Shaanxi Electric Power Company Electric Power Research Institute, Xi’an 710054
Abstract：Partial discharge (PD) in cables is usually caused by cable defects. The type of defect can be quickly judged by effectively recognizing PD signals. In this paper, a new method of partial discharge feature selection based on support vector machine recursive feature elimination (SVM-RFE) and K-means clustering algorithm is proposed. After extracting features from raw data, K-SVM-RFE feature optimization is carried out, and the ranking results of local features of different types of defects are obtained according to the weights, and the results of the optimal ranking are verified. The results show that the effective characteristic parameters of different types of partial discharge signals are the test voltage the product of phase angle and polarity. The validation results under different algorithms show that the feature optimization method of K-SVM-RFE proposed in this paper is an effective method to select the characteristics of PD, which can greatly improve the fault diagnosis rate of cable defects.
李程, 李强, 张启超, 刘子瑞, 李伟. 基于支持向量机递归特征消除的电缆局部放电特征寻优[J]. 电气技术, 2020, 21(1): 67-71.
Li Cheng, Li Qiang, Zhang Qichao, Liu Zirui, Li Wei. Partial discharge characteristic optimization of cables based on support vector machine-recursive feature elimination. Electrical Engineering, 2020, 21(1): 67-71.
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2020, Vol. 21 
