基于最优小波包变换与核主分量分析的局部放电信号特征提取

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Abstract Ultra-high frequency (UHF) method has been widely used for partial discharge (PD) detection in gas insulated switchgear (GIS), but the feature extraction for UHF PD signals is a difficult issue all the while. In this paper, a method using wavelet packet transform (WPT) is proposed to decompose the UHF PD signals, and the best basis is selected using minimum entropy criterion based on UHF PD mathematical model of four typical defects in GIS, then the energy in each frequency range, maximal values of module and absolute average values in each scale are computed according to WP coefficients, and the features space is constructed integrally; Kernel principal component analysis (KPCA) is also proposed for reducing dimension of features, and dimension crisis is resolved well, and the divergence matrix strangeness in every class is eliminated. At the same time, the characteristics of signals are retained the farthest. The classification results show that the features used in this paper are quite well for UHF PD defect identification.

Key words： Partial discharge feature extraction best wavelet packet basis kernel principal component analysis

Received: 05 November 2009 Published: 04 March 2014

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TM835

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	Tang Ju
	Xie Yanbin
	Zhou Qian
	Zhang Xiaoxing

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Tang Ju,Xie Yanbin,Zhou Qian等. Feature Extraction for Partial Discharge Signals Based on the Optimal Wavelet Packet Basis Transform and Kernel Principal Component Analysis[J]. Transactions of China Electrotechnical Society, 2010, 25(9): 35-40.

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http://dgjsxb.ces-transaction.com/EN/ OR http://dgjsxb.ces-transaction.com/EN/Y2010/V25/I9/35

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