Mechanical Fault Diagnosis of High Voltage Circuit Breakers Based on Wavelet Time-Frequency Entropy and One-Class Support Vector Machine

Entropy

Nantian Huang¹  Huaijin Chen¹  Shuxin Zhang¹  Guowei Cai¹  Weiguo Li¹  Dianguo Xu²  Lihua Fang¹ 

[1] School of Electrical Engineering, Northeast Dianli University, Jilin 132012, China;Department of Electrical Engineering, Harbin Institute of Technology, Harbin 150001, China;

关键词: high voltage circuit breakers;    mechanical fault diagnosis;    S-transform;    wavelet time-frequency entropy;    one-class support vector machine;   

DOI  :  10.3390/e18010007

来源: mdpi

【 摘 要 】

Mechanical faults of high voltage circuit breakers (HVCBs) are one of the most important factors that affect the reliability of power system operation. Because of the limitation of a lack of samples of each fault type; some fault conditions can be recognized as a normal condition. The fault diagnosis results of HVCBs seriously affect the operation reliability of the entire power system. In order to improve the fault diagnosis accuracy of HVCBs; a method for mechanical fault diagnosis of HVCBs based on wavelet time-frequency entropy (WTFE) and one-class support vector machine (OCSVM) is proposed. In this method; the S-transform (ST) is proposed to analyze the energy time-frequency distribution of HVCBs’ vibration signals. Then; WTFE is selected as the feature vector that reflects the information characteristics of vibration signals in the time and frequency domains. OCSVM is used for judging whether a mechanical fault of HVCBs has occurred or not. In order to improve the fault detection accuracy; a particle swarm optimization (PSO) algorithm is employed to optimize the parameters of OCSVM; including the window width of the kernel function and error limit. If the mechanical fault is confirmed; a support vector machine (SVM)-based classifier will be used to recognize the fault type. The experiments carried on a real SF₆ HVCB demonstrated the improved effectiveness of the new approach.

【 授权许可】

CC BY   
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