吉林大学学报(工学版) ›› 2013, Vol. 43 ›› Issue (05): 1331-1337.doi: 10.7964/jdxbgxb201305030

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Evaluation of transformer aging based on fuzzy theory and bidirectional associative memory neural networks

MA De-yin, LIANG Yan-chun, GUAN Ren-chu, ZHAO Xiao-she, SHI Xiao-hu   

  1. College of Computer Science and Technology, Jilin University, Changchun 130012, China
  • Received:2012-08-10 Online:2013-09-01 Published:2013-09-01

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Abstract:

A novel method based on fuzzy theory and Bidirectional Associative Memory (BAM) neural networks is proposed to evaluate the transformer aging problem. In this method, first the features of the transformer are fuzzified to deal with their uncertainty problem, and then fuzzy reasoning and defuzzification are performed to evaluate the transformer aging. During the fuzzifying process, because that the electrical characteristics of insulating oil gas and insulating oil are highly personal relied, BAM neural network is used to realize the fuzzification of these two features. To examine the effectiveness of the proposed method, simulation is carried out with real data. Numerical results show that the evaluations of the proposed method highly coincide with the engineering specifications.

Key words: computer application, bidirectional associative memory neural network, fuzzy theory, transformer aging

CLC Number: 

  • TP391.4

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