Insulation defect localization through partial discharge measurements and numerical classification

Partial discharge (PD) analysis is a fundamental tool to guide decision making in electrical insulation diagnosis for condition based maintenance. In this paper, PD signals are analyzed to localize defects in insulation systems. The task of automatic defect localization with respect to electrodes has a wide range of industrial applications. In fact, depending on the apparatus type, risk assessment is remarkably affected by defect location with respect to the electrodes. In this study, various parameters are first extracted from PD distributions, and statistical analysis is performed to select the most significant parameters concerning localization. Then, the localization process is carried out through numerical classification. Three different classification methods are compared to find the best approach for this application. Comparing a k-nearest neighbor classifier, a probabilistic neural network and a support vector machine (SVM) based classifier, the best results are gained with SVM. although the former two are simpler to implement and easier to tune. SVM based classification has not been applied in PD analysis before this research.