Partial Discharge Localization Techniques: A Review of Recent Progress

Jun Qiang Chan, Wong Jee Keen Raymond (), Hazlee Azil Illias and Mohamadariff Othman
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Jun Qiang Chan: Department of Electrical Engineering, Universiti Malaya, Kuala Lumpur 50603, Malaysia
Wong Jee Keen Raymond: Department of Electrical Engineering, Universiti Malaya, Kuala Lumpur 50603, Malaysia
Hazlee Azil Illias: Department of Electrical Engineering, Universiti Malaya, Kuala Lumpur 50603, Malaysia
Mohamadariff Othman: Department of Electrical Engineering, Universiti Malaya, Kuala Lumpur 50603, Malaysia

Energies, 2023, vol. 16, issue 6, 1-31

Abstract: Monitoring the partial discharge (PD) activity of power equipment insulation is crucial to ensure uninterrupted power system operation. PD occurrence is highly correlated to weakened insulation strength. If PD occurrences are left unchecked, unexpected insulation breakdowns may occur. The comprehensive PD diagnostic process includes the detection, localization, and classification of PD. Accurate PD source localization is necessary to locate the weakened insulation segment. As a result, rapid and precise PD localization has become the primary focus of PD diagnosis for power equipment insulation. This paper presents a review of different approaches to PD localization, including conventional, machine learning (ML), and deep learning (DL) as a subset of ML approaches. The review focuses on the ML and DL approaches developed in the past five years, which have shown promising results over conventional approaches. Additionally, PD detection using conventional, unconventional, and a PCB antenna designed based on UHF techniques is presented and discussed. Important benchmarks, such as the sensors used, algorithms employed, algorithms compared, and performances, are summarized in detail. Finally, the suitability of different localization techniques for different power equipment applications is discussed based on their strengths and limitations.

Keywords: partial discharge; localization; machine learning; deep learning; fault diagnostic (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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