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Transformer winding faults classification based on transfer function analysis by support vector machine

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3 Author(s)
Bigdeli, M. ; Dept. of Electr. Eng., Islamic Azad Univ., Zanjan, Iran ; Vakilian, M. ; Rahimpour, E.

This study presents an intelligent fault classification method for identification of transformer winding fault through transfer function (TF) analysis. For this analysis support vector machine (SVM) is used. The required data for training and testing of SVM are obtained by measurement on two groups of transformers (one is a classic 20 kV transformer and the other is a model transformer) under intact condition and under different fault conditions (axial displacement, radial deformation, disc space variation and short circuit of winding). Two different features extracted from the measured TFs are then used as the inputs to SVM classifier for fault classification. The accuracy of proposed method is compared with the accuracy of past well-known works. This comparison indicates that the proposed method can be used as a reliable method for transformer winding fault recognition.

Published in:

Electric Power Applications, IET  (Volume:6 ,  Issue: 5 )