基于2D-LPEWT的特征提取方法在电缆局部放电分析中的应用

doi:10.19595/j.cnki.1000-6753.tces.180057

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Abstract

Partial discharge detection is important for identifying insulation defects of power cables. And the extraction of effective characteristic parameters is the emphasis of the study. In this paper, a feature extraction method based on 2 dimensions Littlewood-Pale empirical wavelet transform (2D-LPEWT) is proposed to realize the accurate identification of different types of defects in cable discharge. By constructing a cable-insulated partial discharge detection platform, three-dimensional spectrum of four typical defect models was decomposed by 2D-LPEWT. And then Tamura, moments and entropy characteristics were extracted from the obtained wavelet coefficients sub-graphs. The effects of different feature extraction methods on performance of K-nearest neighbour (KNN), decision tree and support vector machine (SVM) were discussed. Results show that the proposed feature extraction method can achieve high recognition accuracy under different classifiers, and has good practicability.

Key words： 2 dimensions Littlewood-Pale empirical wavelet transform power cable partial discharge feature extraction classification algorithm

Received: 09 January 2018 Published: 10 January 2019

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TM85

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	Qin Xue
	Qian Yong
	Xu Yongpeng
	Sheng Gehao
	Jiang Xiuchen

Cite this article:

Qin Xue,Qian Yong,Xu Yongpeng等. Application of Feature Extraction Method Based on 2D-LPEWT in Cable Partial Discharge Analysis[J]. Transactions of China Electrotechnical Society, 2019, 34(1): 170-177.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.180057 OR https://dgjsxb.ces-transaction.com/EN/Y2019/V34/I1/170

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