Xin, Chong Wan and Raymond, Wong Jee Keen and Illias, Hazlee Azil and Kin, Lai Weng and Haur, Yiauw Kah (2020) Effects of shorter phase-resolved partial discharge duration on PD classification accuracy. International Journal of Power Electronics and Drive Systems (IJPEDS), 11 (1). pp. 326-332. ISSN 2088-8694, DOI https://doi.org/10.11591/ijpeds.v11.i1.pp326-332.
Full text not available from this repository.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. © 2020, Institute of Advanced Engineering and Science. All rights reserved.
| Item Type: | Article |
|---|---|
| Funders: | Tunku Abdul Rahman University College for supporting this work through TARUC Internal Research Grant (UC/I/G2018-00026), Nvidia Corporation for sponsoring the GPU |
| Uncontrolled Keywords: | Partial discharge; Pattern recognition; PRPD |
| Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Divisions: | Faculty of Engineering |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 09 Jun 2020 06:09 |
| Last Modified: | 09 Jun 2020 06:09 |
| URI: | http://eprints.um.edu.my/id/eprint/24747 |
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