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

Sustainability

Jun Tao¹  Wenmin Yu²  Ren Yu² 

[1] China Nuclear Power Operation Management Co., Ltd., Jiaxing 314300, China;School of Nuclear Science and Techniques, Naval University of Engineering, Wuhan 430034, China;

关键词: main transformer;    condition monitoring;    unsupervised mutual information;    feature selection;    DGA;   

DOI  :  10.3390/su14052700

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown