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All Journal International Journal of Power Electronics and Drive Systems (IJPEDS)
Hazlee Azil Illias
University of Malaya
Control & Systems Engineering Electrical & Electronics Engineering

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Effects of shorter phase-resolved partial discharge duration on PD classification accuracy Chong Wan Xin; Wong Jee Keen Raymond; Hazlee Azil Illias; Lai Weng Kin; Yiauw Kah Haur
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 11, No 1: March 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (439.299 KB) | DOI: 10.11591/ijpeds.v11.i1.pp326-332

Abstract

Partial discharge (PD) pattern recognition is useful to diagnose insulation condition. PD measurement data is commonly represented in phase-resolved partial discharge (PRPD) format. PRPD is useful as it provides a visible pattern for different PD source and various features can be extracted for PD pattern recognition. Shorter PRPD duration will enable more training data but the information in each data is less and vice versa. This works aims to investigate the effects of using very short duration PRPD data on the accuracy of PD pattern recognition. The results conclude that machine learning models such as Artificial Neural Network (ANN) and Support Vector Machine (SVM) are robust enough such that reduction of PRPD duration from 15-seconds to 1-second causes less than 5 % drop in the classification accuracy. However, this is only true for noise free condition. When the same PD data is overlapped with random noise, the classification accuracy suffers a significant reduction up to 19%. Therefore, longer PRPD duration is recommended to withstand the effects of noise contamination.
Co-Authors Chong Wan Xin Lai Weng Kin Wong Jee Keen Raymond Yiauw Kah Haur