Classification of dynamic insulation failures in transformer winding during impulse test using cross-wavelet transform aided foraging algorithm

Abstract

Bacterial foraging-based approach for identification of fault characteristics of dynamic insulation failure in transformer during impulse test has been proposed. The winding currents acquired by tank current method during impulse test are analysed for identification of fault characteristics. The time-frequency domain-based features extracted from cross-wavelet spectra of winding currents of insulation failed and no-fault (healthy) insulation of transformer are given as input to the foraging algorithm for identification of dynamic insulation failure characteristics. The required winding currents to extract the significant features are acquired by emulating different dynamic insulation failures in the developed analogue model of 33 kV winding of 3 MVA transformer. To emulate various fault characteristics in analogue model, suitable fault emulator modules have been developed. Results show that the proposed foraging algorithm with cross-wavelet transform features could successfully identify the fault characteristics of dynamic insulation failure with acceptable accuracy. The classification accuracy of proposed foraging algorithm is also compared with fuzzy c-means classification algorithm. The concepts of dynamic arc model simulation, cross-wavelet transform feature extraction, emulation of dynamic insulation failure in analogue model of transformer and fault characteristics identification are explained.