基于局部放电因子向量和BP神经网络的油纸绝缘老化状况诊断

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Abstract In this paper, thermally accelerated aging is studied experimentally for simulating the environment of the transformer insulation in service. Four statistical spectra and 27 characteristic values are extracted based on the phase-resolved partial discharge (PRPD) pattern of aging sample data. Thereafter, 10 principal component factors are extracted from 27 characteristic values by factor analysis. Furthermore, BP neural network was chosen to diagnose the aging condition of oil-paper insulation based on 10 principal component factors. To train the network, the standard and four improved algorithms of BP network are compared with the training sample data, and then the trained network is used to diagnose the aging condition of the test sample data. It is concluded that BP network based on 10 principal component factors could be used for the aging condition diagnosis of oil-paper insulation to a certain extent. Moreover, it is suggested the L-M algorithm is the most reasonable algorithm for the aging condition diagnosis of oil-paper insulation according to diagnosis results of different algorithms.

Key words： Oil-paper insulation partial discharge transformer aging diagnosis

Received: 05 March 2009 Published: 04 March 2014

PACS:

TM835

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	Articles by authors
	Zhou Tianchun
	Yang Lijun
	Liao Ruijin
	Wang Ke
	Zheng Shengxun

Cite this article:

Zhou Tianchun,Yang Lijun,Liao Ruijin等. Diagnosis of Aging Condition in Oil-Paper Insulation Based on Factor Vectors of Partial Discharge and BP Neural Network[J]. Transactions of China Electrotechnical Society, 2010, 25(10): 18-23.

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http://dgjsxb.ces-transaction.com/EN/ OR http://dgjsxb.ces-transaction.com/EN/Y2010/V25/I10/18

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