Comparison of support vector machine based partial discharge identification parameters

Partial discharge (PD) may have a significant effect on the insulation performance of power apparatus. Therefore, identification of PD sources is of interest to both power equipment manufacturers and utilities. With the development of PD measurement techniques, data analysis, signal processing and pattern recognition are gaining more interest. Research to date has considered varieties of different identification parameters such as phase resolved information, statistical operators, pulse shape analysis, pulse sequence analysis, frequency spectrum and wavelet analysis, which are also combined with so-called classifiers such as fuzzy logic, neural networks (NN) and learning machines. This paper investigates the performances of PD source identification using a support vector machine (SVM) based on different feature parameters. Due to the unique characteristics of SVM, some feature parameters that are not suitable for other classifiers are applicable. In this paper, comparisons of recognition rate and generalization capability between different feature parameters are discussed. Investigation reveals that recognition rate and generalization capability are influenced by the input PD parameters. Initial results indicate that, by using appropriate kernels and feature parameters, the automatic identification results obtained by using the support vector machine technique are very encouraging.