A lower dimensional feature vector for identification of partial discharges of different origin using time measurements

Abstract

Partial discharge (PD) classification into sources of different origin is essential in evaluating the severity of the damage caused by its activity on the insulation of power cables and their accessories. More specifically, some types of PD can be classified as having a detrimental effect on the integrity of the insulation while others can be deemed relatively harmless, rendering the correct classification of different PD types of vital importance to electrical utilities. In this work, a feature vector was proposed based on higher order statistics on selected nodes of the wavelet packet transform (WPT) coefficients of time domain measurements, which can compactly represent the characteristics of different PD sources. To assess its performance, experimental data acquired under laboratory conditions for four different PD sources encountered in power systems were used. The two learning machine methods, namely the support vector machine and the probabilistic neural network, employed as the classification algorithms, achieved overall classification rates of around 98%. In comparison, the utilization of the scaled, raw WPT coefficients as a feature vector resulted in classification accuracy of around 99%, but with a significantly higher number of dimensions (1304 to 16), validating the PD identification ability of the proposed feature. Dimensionality reduction becomes a key factor in online, real-time data collection and processing of PD measurements, reducing the classification effort and the data-storage requirements. Therefore, the proposed method can constitute a potential tool for such online measurements, after addressing issues related to on-site measurements such as the rejection of interference

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Identifiers

ORCID for Paul L. Lewin: orcid.org/0000-0002-3299-2556

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