基于小波变换的交流系统串联电弧故障诊断

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Abstract For diagnosing the series arc fault in the power system accurately, according to the characteristic of series arc fault in AC system, this paper presents a method of series arc fault analysis and diagnosis based on the wavelet transform using the experimental platform of arc fault and arcing fault experiment in different loads. Combined with the wavelet transform module maxima and multi-resolution analysis, the db3 wavelet function is used to extract the feature vector in each frequency band of both the normal and fault signals which are denoised by the minimax principle. With the improved BP neural network based on the Levenberg-Marquarat, the fault is diagnosed by the mapping relationship between the feature vector and arc fault. The result of test shows that the proposed method can realize the diagnosis and classification of the series arc fault in AC system effectively.

Key words： Arc fault wavelet transform neural network module maxima fault diagnosis

Received: 11 October 2012 Published: 16 June 2014

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TM501

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	Articles by authors
	Liu Xiaoming
	Zhao Yang
	Cao Yundong
	Hou Chunguang
	Wang Lijun

Cite this article:

Liu Xiaoming,Zhao Yang,Cao Yundong等. Series Arc Fault Diagnosis Based on Wavelet Transform in AC System[J]. Transactions of China Electrotechnical Society, 2014, 29(1): 10-17.

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http://dgjsxb.ces-transaction.com/EN/ OR http://dgjsxb.ces-transaction.com/EN/Y2014/V29/I1/10

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