Cross-correlation aided wavelet network for classification of dynamic insulation failures in transformer winding during impulse test

Wavelet network based approach for identification of fault characteristics of dynamic insulation failure during impulse test has been proposed. The network identifies the fault characteristics using the significant features extracted from cross-correlation sequence of winding currents of no-fault as well as impulse faulted winding insulation. The required winding current waveforms to extract significant features for identification of various fault characteristics are acquired by emulating different dynamic insulation failures in the analog model of 33 kV winding of 3 MVA transformer using developed analog fault simulator. The results show that the wavelet network using cross-correlation features has successfully identified the dynamic insulation failure characteristics, viz. fault type, condition and location of occurrence of failure along the length of the winding with acceptable accuracy. The efficacy of extracted features and developed wavelet network for fault characteristics identification is also compared with artificial neural network classifier. The concept of emulation of dynamic insulation failure, cross-correlation based feature extraction and wavelet based fault characteristics identification methods are explained.