以提升小波包系数熵为特征值的隐马尔科夫电缆局部放电识别

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Abstract For the detection of partial discharge in cables, this paper presents a recognition method based on lifting wavelet packet coefficient entropy and hidden Markov model. Based on the theory of lifting wavelet packet and information entropy, the wavelet energy spectrum entropy and coefficient entropy of discharge signal are extracted as eigenvalues. The extracted eigenvectors are input into the hidden Markov model for training, and the optimal training model is obtained. Artificial simulation of defects on cable body, discharges generated by different discharge models are identified and tested by using the proposed algorithm, traditional wavelet coefficient entropy and BP neural network respectively. The results show that the method is superior to the traditional wavelet and BP neural network in recognition accuracy and algorithm execution efficiency.

Key words： lifting wavelet packet hidden Markov partial discharge wavelet coefficient entropy

Received: 09 March 2020

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	Articles by authors
	Qian Shuaiwei
	Peng Yanjun
	Zhou Zemin
	Chen Jianxi
	Tang Ming

Cite this article:

Qian Shuaiwei,Peng Yanjun,Zhou Zemin等. Partial discharge pattern recognition using hidden Markov models based on the entropy lifting wavelet coefficients[J]. Electrical Engineering, 2020, 21(10): 93-102.

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http://dqjs.cesmedia.cn/EN/Y2020/V21/I10/93

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