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Radiometric Partial Discharge Detection: A Review

Author

Listed:
  • Sinda Kaziz

    (Faculté des Sciences de Monastir, Université de Monastir, Monastir 5019, Tunisia
    L.E.PR.E. H.V. Laboratory, Department of Engineering, University of Palermo, 90128 Palermo, Italy)

  • Mohamed Hadj Said

    (Centre de Recherche en Microélectronique et Nanotechnologie (CRMN), Sousse 4050, Tunisia)

  • Antonino Imburgia

    (L.E.PR.E. H.V. Laboratory, Department of Engineering, University of Palermo, 90128 Palermo, Italy)

  • Bilel Maamer

    (Faculté des Sciences de Monastir, Université de Monastir, Monastir 5019, Tunisia
    Centre de Recherche en Microélectronique et Nanotechnologie (CRMN), Sousse 4050, Tunisia)

  • Denis Flandre

    (SMALL Group, ICTEAM Institute, UCLouvain, 1348 Louvain-la-Neuve, Belgium)

  • Pietro Romano

    (L.E.PR.E. H.V. Laboratory, Department of Engineering, University of Palermo, 90128 Palermo, Italy)

  • Fares Tounsi

    (SMALL Group, ICTEAM Institute, UCLouvain, 1348 Louvain-la-Neuve, Belgium)

Abstract

One of the most common failures or breakdowns that can occur in high-voltage (HV) equipment is due to partial discharges (PDs). This occurs as a result of inadequate insulation, aging, harsh environmental effects, or manufacturing flaws. PD detection and recognition methods have gained growing attention and have seen great progress in the past decades. Radiometric methods are one of the most investigated detection approaches due to their immunity to electromagnetic interference (EMI) and their capabilities to detect and locate PD activities in different applications such as transformers, cables, etc. Several review articles have been published to classify and categorize these works. Nonetheless, some concepts are missing, and some improvement techniques, such as PD detection at high-frequency (HF) and very high-frequency (VHF), have been overlooked. We present in this paper an exhaustive review study of state-of-the-art PD detection based on radiometric methods at different usable radiofrequency bands (i.e., HF, VHF, and UHF). Accordingly, we propose a new generic categorization approach based on the detected electromagnetic wave component (magnetic or electric fields) and pick-up location, either from free space or ground cable.

Suggested Citation

  • Sinda Kaziz & Mohamed Hadj Said & Antonino Imburgia & Bilel Maamer & Denis Flandre & Pietro Romano & Fares Tounsi, 2023. "Radiometric Partial Discharge Detection: A Review," Energies, MDPI, vol. 16(4), pages 1-33, February.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:4:p:1978-:d:1070978
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    References listed on IDEAS

    as
    1. Jian Li & Xudong Li & Lin Du & Min Cao & Guochao Qian, 2016. "An Intelligent Sensor for the Ultra-High-Frequency Partial Discharge Online Monitoring of Power Transformers," Energies, MDPI, vol. 9(5), pages 1-15, May.
    2. 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.
    3. Ghulam Amjad Hussain & Ashraf A. Zaher & Detlef Hummes & Madia Safdar & Matti Lehtonen, 2020. "Hybrid Sensing of Internal and Surface Partial Discharges in Air-Insulated Medium Voltage Switchgear," Energies, MDPI, vol. 13(7), pages 1-16, April.
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    Cited by:

    1. Michał Kozioł & Łukasz Nagi & Tomasz Boczar & Zbigniew Nadolny, 2023. "Quantitative Analysis of Surface Partial Discharges through Radio Frequency and Ultraviolet Signal Measurements," Energies, MDPI, vol. 16(9), pages 1-15, April.
    2. Marek Florkowski, 2023. "Effect of Interplay between Parallel and Perpendicular Magnetic and Electric Fields on Partial Discharges," Energies, MDPI, vol. 16(13), pages 1-16, June.
    3. Marek Florkowski, 2024. "Comparison of Effects of Partial Discharge Echo in Various High-Voltage Insulation Systems," Energies, MDPI, vol. 17(20), pages 1-17, October.
    4. Haresh Kumar & Muhammad Shafiq & Kimmo Kauhaniemi & Mohammed Elmusrati, 2024. "A Review on the Classification of Partial Discharges in Medium-Voltage Cables: Detection, Feature Extraction, Artificial Intelligence-Based Classification, and Optimization Techniques," Energies, MDPI, vol. 17(5), pages 1-31, February.

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