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Partial Discharge Localization Techniques: A Review of Recent Progress

Author

Listed:
  • 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)

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.

Suggested Citation

  • Jun Qiang Chan & Wong Jee Keen Raymond & Hazlee Azil Illias & Mohamadariff Othman, 2023. "Partial Discharge Localization Techniques: A Review of Recent Progress," Energies, MDPI, vol. 16(6), pages 1-31, March.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:6:p:2863-:d:1102035
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    References listed on IDEAS

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    1. Sonia Barrios & David Buldain & María Paz Comech & Ian Gilbert & Iñaki Orue, 2019. "Partial Discharge Classification Using Deep Learning Methods—Survey of Recent Progress," Energies, MDPI, vol. 12(13), pages 1-16, June.
    2. Iago Búa-Núñez & Julio E. Posada-Román & José A. García-Souto, 2021. "Multichannel Detection of Acoustic Emissions and Localization of the Source with External and Internal Sensors for Partial Discharge Monitoring of Power Transformers," Energies, MDPI, vol. 14(23), pages 1-20, November.
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    Cited by:

    1. Feng Liu & Jian Du & Yansheng Shi & Shuainan Zhang & Wei Wang, 2024. "Localization of Dual Partial Discharge in Transformer Windings Using Fabry–Pérot Optical Fiber Sensor Array," Energies, MDPI, vol. 17(11), pages 1-12, May.

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