Inderscience Publishers

Classification of impulse fault response patterns in transformers using cascaded wavelet network

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Accurate identification of winding faults that may develop during impulse testing of transformer is of great importance for ensuring reliability and quality of power supply. In the present work a wavelet network based methodology has been proposed for feature extraction as well as classification of those features for impulse fault pattern identification. The classification involves two networks, viz. a single dimensional wavelet network (SDWN) for feature extraction and a multi dimensional wavelet network (MDWN) for analysing the extracted features of the optimally trained SDWN to classify the fault type and location along the transformer winding. Results show that the proposed methodology can perform efficiently for classification of various types of impulse faults in transformers.

Keywords: impulse fault patterns, classification, wavelet networks, transformer modelling, winding faults, transformers, power supply reliability, power supply quality, feature extraction

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