基于交叉小波变换和相关系数矩阵的局部放电特征提取

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Abstract A new method for partial discharge feature extraction based on cross-wavelet and correlation coefficient matrix is proposed aimed at the characteristic quantities’ high-dimension and high-sensitive to noise. Cross-wavelet transform method gives analysis between time and frequency domain for signals. The method is immune to the noise and it is applied for power transformer partial discharge signal analysis. New characteristic quantities are obtained representing cross- wavelet spectrum. And then correlation coefficient matrix is constructed for correlation analysis of extracted characteristic quantities, and those which have similar classification ability are eliminated. Finally probabilistic neural networks and back propagation neural network classifiers are utilized for pattern recognition. Simulation results demonstrate that the proposed method is effective.

Key words： Cross-wavelet power transformer partial discharge feature extraction correlation coefficient matrix

Received: 04 January 2013 Published: 27 June 2014

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TM835

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	Articles by authors
	Shang Haikun
	Yuan Jinsha
	Wang Yu
	Jin Song

Cite this article:

Shang Haikun,Yuan Jinsha,Wang Yu等. Feature Extraction for Partial Discharge Based on Cross-Wavelet Transform and Correlation Coefficient Matrix[J]. Transactions of China Electrotechnical Society, 2014, 29(4): 274-281.

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http://dgjsxb.ces-transaction.com/EN/ OR http://dgjsxb.ces-transaction.com/EN/Y2014/V29/I4/274

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