Partial discharge pattern recognition using multiscale feature extraction and support vector machine

An accurate interpretation of partial discharge (PD) signals in high voltage (HV) equipment provides crucial information for assessing the insulation conditions. To automate the interpretation process, feature extraction of PD signals and pattern recognition using the extracted features are required. This paper adopts discrete wavelet transform (DWT) and empirical mode decomposition (EMD) for signal decomposition and feature extraction on the PD signals obtained from different insulation defects. Support vector machine (SVM) is then used for classifying the features. Results indicate that features extracted from decomposed signals provide higher classification accuracy when compared with the conventional method that the features are extracted from original PD signals.