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