Statistical pattern analysis of partial discharge measurements for quality assessment of insulation systems in high-voltage electrical machinery

In this paper, we present a new statistical analysis method of phase-resolved partial discharge (PD) measurements for the quality assessment of electrical insulation in high-voltage machinery. The method is based on a supervised classification approach which utilizes histogram similarity analysis. The motivation for choosing histogram similarity analysis is twofold. First, the phase-resolved PD measurement itself is, in fact, a two-dimensional histogram. Therefore, a histogram-matching-based approach suits the very nature of the data. Second, histogram similarity analysis combines the typical statistical parameters, used in PD analysis, in a statistically powerful and rigorous way. In our study, we utilize various histogram types and similarity analysis, including correlation, chi-square, and Kolmogorov-Smirnov tests. Further, we propose a postprocessing method to quantify the accuracy of classification results which enables the user to make soft decisions. Our experimental study on laboratory samples demonstrates that the method shows strong potential in detection and classification of insulation defects. The results from our study suggest that the proposed method provides a powerful, general, and mathematically simple approach to the analysis of phase-resolved PD measurements.