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Entropy 2016, 18(1), 7; doi:10.3390/e18010007

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

1
School of Electrical Engineering, Northeast Dianli University, Jilin 132012, China
2
Department of Electrical Engineering, Harbin Institute of Technology, Harbin 150001, China
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Academic Editor: Carlo Cattani
Received: 4 November 2015 / Revised: 7 December 2015 / Accepted: 17 December 2015 / Published: 26 December 2015
(This article belongs to the Special Issue Wavelets, Fractals and Information Theory)
View Full-Text   |   Download PDF [3204 KB, uploaded 26 December 2015]   |  

Abstract

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. View Full-Text
Keywords: high voltage circuit breakers; mechanical fault diagnosis; S-transform; wavelet time-frequency entropy; one-class support vector machine high voltage circuit breakers; mechanical fault diagnosis; S-transform; wavelet time-frequency entropy; one-class support vector machine
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Huang, N.; Chen, H.; Zhang, S.; Cai, G.; Li, W.; Xu, D.; Fang, L. Mechanical Fault Diagnosis of High Voltage Circuit Breakers Based on Wavelet Time-Frequency Entropy and One-Class Support Vector Machine. Entropy 2016, 18, 7.

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