GIS局部放电脉冲分类特征提取算法

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Abstract The partial discharge (PD) pulse group classification spectrum constructed based on traditional PD time-frequency information can only provide low-dimensional feature characteristics of PD pluses.When the classification algorithm requires more characteristics of PD pluses to complete the classification work,the abovementioned methods do not work well.This article presents an equivalent time-frequency entropy algorithm to extract the multidimensional characteristics which present the PD pluses waveform feather,and then constructs the PD pluses groups equivalent time-frequency entropy classification spectrum.The spectrum is further combined with the improved fuzzy C means clustering algorithm to complete the classification work of different types of PD pluses groups.The testing results based on gas insulated switches (GIS) prove the validity and rationality of this algorithm,which provides both experimental and theoretical basis for the development of PD online monitoring and identification system based on single artificial defect model.

Key words： Partial discharge fuzzy C means clustering algorithm feature extraction online detection equivalent time-frequency entropy

Received: 12 April 2015 Published: 07 July 2016

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TM835

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	Bao Yongsheng
	Hao Fengjie
	Xu Jianzhong
	Zhang Yuanhang

Cite this article:

Bao Yongsheng,Hao Fengjie,Xu Jianzhong等. Classification Feature Extraction Algorithm for GIS Partial Discharge Pulses[J]. Transactions of China Electrotechnical Society, 2016, 31(9): 181-188.

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https://dgjsxb.ces-transaction.com/EN/ OR https://dgjsxb.ces-transaction.com/EN/Y2016/V31/I9/181

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