Article: Studies on three feature extraction methods for the location and classification of dynamic fault patterns during impulse testing of transformer winding Journal: International Journal of Power and Energy Conversion (IJPEC) 2012 Vol.3 No.3/4 pp.265 - 286 Abstract: Many computer aided classifiers have been employed for identification of impulse insulation failure parameters, viz. type, condition of insulation and location using significant features extracted from winding current. Classification accuracy of these classifiers is dependent on the ability of extracted features. In this present approach, an attempt has been made to identify suitable feature extractor for accurate classification of insulation failure using simple classifier. The suitability of three feature extraction methods, viz. cross-wavelet transform (XWT), discrete wavelet transform (DWT) and cross-correlation (CCL) are assessed for identification of failure using differential evolution (DE) classifier. The required winding currents for feature extraction are acquired by emulating different insulation failure in an analogue model of 33 kV winding of 3 MVA transformer. Result of developed DE classifier shows that XWT features identified the insulation failure more accurately than DWT and CCL. Inderscience Publishers - linking academia, business and industry through research

Title: Studies on three feature extraction methods for the location and classification of dynamic fault patterns during impulse testing of transformer winding

Authors: P. Rajamani; Sivaji Chakravorti

Addresses: Department of Electrical Engineering, Jadavpur University, 188, Raja S.C. Mallik Road, Jadavpur, Kolkata - 700032, West Bengal, India. ' Department of Electrical Engineering, Jadavpur University, 188, Raja S.C. Mallik Road, Jadavpur, Kolkata - 700032, West Bengal, India

Abstract: Many computer aided classifiers have been employed for identification of impulse insulation failure parameters, viz. type, condition of insulation and location using significant features extracted from winding current. Classification accuracy of these classifiers is dependent on the ability of extracted features. In this present approach, an attempt has been made to identify suitable feature extractor for accurate classification of insulation failure using simple classifier. The suitability of three feature extraction methods, viz. cross-wavelet transform (XWT), discrete wavelet transform (DWT) and cross-correlation (CCL) are assessed for identification of failure using differential evolution (DE) classifier. The required winding currents for feature extraction are acquired by emulating different insulation failure in an analogue model of 33 kV winding of 3 MVA transformer. Result of developed DE classifier shows that XWT features identified the insulation failure more accurately than DWT and CCL.

Keywords: feature extraction; dynamic impulse insulation failure; discrete wavelet transform; DWT; cross-wavelet transform; XWT; cross-correlation; CCL; differential evolution; dynamic fault patterns; impulse testing; transformer winding; classification accuracy.

DOI: 10.1504/IJPEC.2012.048044

International Journal of Power and Energy Conversion, 2012 Vol.3 No.3/4, pp.265 - 286

Received: 05 Jul 2011
Accepted: 16 Aug 2011
Published online: 07 Aug 2014 *

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Title: Studies on three feature extraction methods for the location and classification of dynamic fault patterns during impulse testing of transformer winding

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