基于密度峰值聚类算法的局部放电脉冲分割

doi:10.19595/j.cnki.1000-6753.tces.190020

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Abstract Partial discharge (PD) signal processing is the basis of insulation state evaluation of electric power equipment, and feature extraction is the key link of signal processing. The feature extraction includes two steps: partial discharge pulse segmentation and discharge feature extraction. Pulse segmentation extraction is the premise of subsequent feature extraction and fault classification of PD signals. In order to preserve discharge information as much as possible and reduce manual interference, a PD pulse segmentation method based on clustering algorithm is proposed in this paper. The wavelet decomposition algorithm is used for filtering, and the noise rejection ratio (NRR) is used to characterize the filtering effect. The method takes all the partial discharge half-wave pulses into consideration to calculate the energy of each half-wave which is the integral of squared instant value of the PD signal over time. Therefore, it can describe the process of partial discharge more accurately. The Otsu algorithm is used to calculate the energy threshold adaptively, and one density peak clustering algorithm, called as clustering by fast search and find of density peaks (DPC), is used to achieve automatic segmentation of PD pulses. Three types of partial discharge models are established in the laboratory. Data from 10 sets of corona discharge, 11 sets of suspension discharge and 30 sets of cone-plate discharge are collected to verify the method. The results have achieved a recognition rate of more than 80%, which is higher or equivalent to the existing similar algorithms, demonstrating the superiority of this method.

Key words： Partial discharge Otsu adaptive energy threshold adaptive energy threshold pulse segment clustering by fast search and find of density peaks (DPC)

Received: 03 January 2019 Published: 27 March 2020

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TM85

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	Zhu Yongli
	Jiang Wei
	Liu Gang

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Zhu Yongli,Jiang Wei,Liu Gang. Partial Discharge Pulse Segmentation Based on Clustering by Fast Search and Find of Density Peaks[J]. Transactions of China Electrotechnical Society, 2020, 35(6): 1377-1386.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.190020 OR https://dgjsxb.ces-transaction.com/EN/Y2020/V35/I6/1377

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