An Unsupervised Mutual Information Feature Selection Method Based on SVM for Main Transformer Condition Diagnosis in Nuclear Power Plants

Wenmin Yu, Ren Yu and Jun Tao
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Wenmin Yu: School of Nuclear Science and Techniques, Naval University of Engineering, Wuhan 430034, China
Ren Yu: School of Nuclear Science and Techniques, Naval University of Engineering, Wuhan 430034, China
Jun Tao: China Nuclear Power Operation Management Co., Ltd., Jiaxing 314300, China

Sustainability, 2022, vol. 14, issue 5, 1-10

Abstract: Dissolved gas in oil (DGA) is a common means of monitoring the condition of an oil-immersed transformer. The concentration of dissolved gas and the ratio of different gases are important indexes to judge the condition of power transformers. Monitoring devices for dissolved gas in oil are widely installed in main transformers, but there are few recorded fault data of main transformers. The special operation and maintenance modes of main transformers leads to the fault modes particularity of main transformers. In order to solve the problem of insufficient samples and the feature uncertainty, this paper puts forward an unsupervised mutual information method to select the feature verified by the optimized support vector machine (SVM) model of particle swarm optimization (PSO) method and tries to find the feature sequence with better performance. The methos is validated by data from nuclear power transformers.

Keywords: main transformer; condition monitoring; unsupervised mutual information; feature selection; DGA (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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