Affiliations: [a] College of Electrical Engineering, Zhejiang University, Hangzhou, China | [b] DESTEC Department, University of Pisa, Pisa, Italy
Correspondence:
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Corresponding author: Sami Barmada, DESTEC Department, University of Pisa, Pisa, Italy. E-mail: [email protected]
Abstract: In this contribution a methodology to diagnose transformer faults based on Dissolved Gas Analysis (DGA) by using a convolutional neural network (CNN) is proposed. The algorithm to transform the gas contents (resulting from the DGA analysis) into feature maps is introduced, and the resulting feature maps are the input of the CNN. In order to take into account the fact that the data set is imbalanced, the improved Synthetic Minority Over-Sampling Technique (SMOTE) is combined with the data cleaning technique to protect the CNN from training bias. The effect of the CNN architecture on the classification performance is also investigated to determine the optimal CNN parameters. All the above mentioned possibilities are tested and their performance investigated; in addition, a final test on the IEC TC 10 transformer fault database validates the accuracy and the generalization potential of the proposed methodology.
