基于改进小波能熵和支持向量机的短时电能质量扰动识别

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Abstract This paper proposes an approach to identify short-time power quality disturbances based on improved wavelet energy entropy and support vector machine (SVM). Firstly, the sampled signals are processed by using the multi-scale wavelet resolution and reconstruction. Then, the sliding time window is introduced into algorithm, combined the time domain analysis with frequency domain analysis. The wavelet coefficients of the high frequency regions are selected for feature extraction. The values of the entropy are then calculated according to the improved wavelet energy entropy proposed in this paper as the disturbances features. Furthermore, these features are used as the input vectors of SVM to classify the short-time power quality disturbances. The simulation results show that the proposed method has merits over the conventional wavelet energy entropy approach.

Key words： Power quality disturbance identification improved wavelet energy entropy SVM

Received: 14 August 2007 Published: 13 February 2014

PACS:

TM714.3

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	Li Gengyin
	Wang Honglei
	Zhou Ming

Cite this article:

Li Gengyin,Wang Honglei,Zhou Ming. Short-Time Power Quality Disturbances Identification Based on Improved Wavelet Energy Entropy and SVM[J]. Transactions of China Electrotechnical Society, 2009, 24(4): 161-167.

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http://dgjsxb.ces-transaction.com/EN/ OR http://dgjsxb.ces-transaction.com/EN/Y2009/V24/I4/161

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