Partial discharge sources classification of power transformer using pattern recognition techniques

Continuous Partial discharge (PD) monitoring can help assess the integrity of transformer insulation system. Over the past few decades, various aspects of PD techniques have been investigated. Current research of PD focuses on multiple PD sources classification, which aims to identify the types of several defects that may coexist in a transformer and cause discharge. This paper develops a hybrid discrete wavelet transform (DWT) and support vector machine (SVM) algorithm targeting multiple PD sources classification. To evaluate the performance of this algorithm, experiments on a number of artificial PD models and transformers are conducted in the paper.