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A Review on the Classification of Partial Discharges in Medium-Voltage Cables: Detection, Feature Extraction, Artificial Intelligence-Based Classification, and Optimization Techniques

Author

Listed:
  • Haresh Kumar

    (School of Technology and Innovation, University of Vaasa, 65200 Vaasa, Finland)

  • Muhammad Shafiq

    (Center for Advanced Power Systems, Florida State University, Tallahassee, FL 32310, USA)

  • Kimmo Kauhaniemi

    (School of Technology and Innovation, University of Vaasa, 65200 Vaasa, Finland)

  • Mohammed Elmusrati

    (School of Technology and Innovation, University of Vaasa, 65200 Vaasa, Finland)

Abstract

Medium-voltage (MV) cables often experience a shortened lifespan attributed to insulation breakdown resulting from accelerated aging and anomalous operational and environmental stresses. While partial discharge (PD) measurements serve as valuable tools for assessing the insulation state, complexity arises from the presence of diverse discharge sources, making the evaluation of PD data challenging. The reliability of diagnostics for MV cables hinges on the precise interpretation of PD activity. To streamline the repair and maintenance of cables, it becomes crucial to discern and categorize PD types accurately. This paper presents a comprehensive review encompassing the realms of detection, feature extraction, artificial intelligence, and optimization techniques employed in the classification of PD signals/sources. Its exploration encompasses a variety of sensors utilized for PD detection, data processing methodologies for efficient feature extraction, optimization techniques dedicated to selecting optimal features, and artificial intelligence-based approaches for the classification of PD sources. This synthesized review not only serves as a valuable reference for researchers engaged in the application of methods for PD signal classification but also sheds light on potential avenues for future developments of techniques within the context of MV cables.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:5:p:1142-:d:1347557
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    References listed on IDEAS

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    1. Abdullahi Abubakar Mas’ud & Ricardo Albarracín & Jorge Alfredo Ardila-Rey & Firdaus Muhammad-Sukki & Hazlee Azil Illias & Nurul Aini Bani & Abu Bakar Munir, 2016. "Artificial Neural Network Application for Partial Discharge Recognition: Survey and Future Directions," Energies, MDPI, vol. 9(8), pages 1-18, July.
    2. 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.
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