Partial discharge identification using a support vector machine

Abstract

Partial discharge (PD) on-line monitoring and diagnosis is an important tool to assess the condition of power equipment. Different PD sources have different effects on the insulation performance of power apparatus. Therefore, identification of PD sources is of interest to both the power equipment manufacturers and utilities. A method based on machine learning theory, namely the support vector machine (SVM) was used for PD identification. Obtained experimental results from different partial discharge sources were pre-processed by using phase based information and wavelet analysis. Pre-processed data were also used as the SVM's input vectors, which was initially trained by known discharge source data, and then applied to identify different types of discharge sources. Initial results indicate that, by using appropriate kernels and parameters, the automatic identification results obtained using the SVM technique is encouraging.