Affiliations: [a] College of Electrical Engineering, Zhejiang University, West Lake, Hangzhou, China | [b] College of Mechanical and Electrical Engineering, China Jiliang University, Qiantang, Hangzhou, China
Correspondence:
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Corresponding author: Shiyou Yang, College of Electrical Engineering, Zhejiang University, Hangzhou, No. 38, Zheda Road, West Lake District, Hangzhou, China. E-mail: eesyyang@zju.edu.cn
Abstract: An artificial neural network (ANN) based methodology to diagnose transformer faults is proposed. The synthetic minority over-sampling technique (SMOTE) is used to solve the imbalance in the dataset. The SMOTE is improved by introducing a full cycle of creating synthetic samples from minority class samples for the goals that the over-sampled ratio can be automatically determined and the sample size of each category can be completely consistent. The contents of dissolved gases in transformer oils are treated as the original features. The optimal features combination for ANN is determined by comparing the performances of the ANN when different feature combinations are used. The performances of different activation functions used in the ANN are investigated to give the optimal one. The tested results show the high accuracy (97.92%) of the proposed methodology if the optimum feature combination and activation function are used.
