粒子群优化自适应小波神经网络在带电局放信号识别中的应用

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Abstract The (partial discharge, PD) signal of XLPE cable may come not only from the body of the cable and its termination, but also from the corona discharge or surface discharge of the switchgear connected with the cable. Different PD sources have different damages on the equipment, and their differentiating criteria are different too. Therefore it is necessary to recognize different PDs. A new pattern recognition method based on adaptive wavelet neural network is proposed and an adaptive wavelet neural network with four-layer is given to recognize PD source in this paper. Particle swarm optimization algorithm is used to optimize the network first, and then BP algorithm is used to make a second optimization, whose performance is remarkably better than that only using BP algorithm. Influences of different wavelets and different structures to the performance of the adaptive wavelet neural network are discussed. The results show that the adaptive wavelet neural network which is optimized by both particle swarm optimization and BP algorithms is able to recognize PD source accurately and reliably.

Key words： Partial discharge online detection adaptive wavelet neural network pattern recognition particle swarm optimization algorithm

Received: 20 October 2013 Published: 05 November 2014

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TM831

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	Articles by authors
	Luo Xin
	Niu Haiqing
	Lai Liyong
	Shen Yangyang
	Wu Qian

Cite this article:

Luo Xin,Niu Haiqing,Lai Liyong等. Application of Adaptive Wavelet Neural Network Based on Particle Swarm Optimization Algorithm in Online PD Pattern Recognition[J]. Transactions of China Electrotechnical Society, 2014, 29(10): 326-333.

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http://dgjsxb.ces-transaction.com/EN/ OR http://dgjsxb.ces-transaction.com/EN/Y2014/V29/I10/326

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