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Review on Artificial Intelligence-Based Fault Location Methods in Power Distribution Networks

Author

Listed:
  • Hamed Rezapour

    (Centre of Excellence for Power System Automation and Operation, School of Electrical Engineering, Iran University of Science and Technology, Tehran 16846-13114, Iran)

  • Sadegh Jamali

    (Centre of Excellence for Power System Automation and Operation, School of Electrical Engineering, Iran University of Science and Technology, Tehran 16846-13114, Iran)

  • Alireza Bahmanyar

    (Department of Electrical Engineering, Computer Science Montefiore Institute, University of Liège, 4000 Liège, Belgium)

Abstract

This paper provides a comprehensive and systematic review of fault localization methods based on artificial intelligence (AI) in power distribution networks described in the literature. The review is organized into several sections that cover different aspects of the methods proposed. It first discusses the advantages and disadvantages of various techniques used, including neural networks, fuzzy logic, and reinforcement learning. The paper then compares the types of input and output data generated by these algorithms. The review also analyzes the data-gathering systems, including the sensors and measurement equipment used to collect data for fault diagnosis. In addition, it discusses fault type and DG considerations, which, together with the data-gathering systems, determine the applicability range of the methods. Finally, the paper concludes with a discussion of future trends and research gaps in the field of AI-based fault location methods. Highlighting the advantages, limitations, and requirements of current AI-based methods, this review can serve the researchers working in the field of fault location in power systems to select the most appropriate method based on their distribution system and requirements, and to identify the key areas for future research.

Suggested Citation

  • Hamed Rezapour & Sadegh Jamali & Alireza Bahmanyar, 2023. "Review on Artificial Intelligence-Based Fault Location Methods in Power Distribution Networks," Energies, MDPI, vol. 16(12), pages 1-18, June.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:12:p:4636-:d:1168479
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    References listed on IDEAS

    as
    1. Alireza Forouzesh & Mohammad S. Golsorkhi & Mehdi Savaghebi & Mehdi Baharizadeh, 2021. "Support Vector Machine Based Fault Location Identification in Microgrids Using Interharmonic Injection," Energies, MDPI, vol. 14(8), pages 1-14, April.
    2. Mostafa Bakkar & Santiago Bogarra & Felipe Córcoles & Ahmed Aboelhassan & Shuo Wang & Javier Iglesias, 2022. "Artificial Intelligence-Based Protection for Smart Grids," Energies, MDPI, vol. 15(13), pages 1-18, July.
    3. Md Shafiullah & M. A. Abido & Taher Abdel-Fattah, 2018. "Distribution Grids Fault Location employing ST based Optimized Machine Learning Approach," Energies, MDPI, vol. 11(9), pages 1-23, September.
    4. Enrique Personal & Antonio García & Antonio Parejo & Diego Francisco Larios & Félix Biscarri & Carlos León, 2016. "A Comparison of Impedance-Based Fault Location Methods for Power Underground Distribution Systems," Energies, MDPI, vol. 9(12), pages 1-30, December.
    5. Jorge De La Cruz & Eduardo Gómez-Luna & Majid Ali & Juan C. Vasquez & Josep M. Guerrero, 2023. "Fault Location for Distribution Smart Grids: Literature Overview, Challenges, Solutions, and Future Trends," Energies, MDPI, vol. 16(5), pages 1-37, February.
    6. Gian Paramo & Arturo Bretas & Sean Meyn, 2022. "Research Trends and Applications of PMUs," Energies, MDPI, vol. 15(15), pages 1-32, July.
    7. Raad Salih Jawad & Hafedh Abid, 2023. "HVDC Fault Detection and Classification with Artificial Neural Network Based on ACO-DWT Method," Energies, MDPI, vol. 16(3), pages 1-18, January.
    8. Sapountzoglou, Nikolaos & Lago, Jesus & De Schutter, Bart & Raison, Bertrand, 2020. "A generalizable and sensor-independent deep learning method for fault detection and location in low-voltage distribution grids," Applied Energy, Elsevier, vol. 276(C).
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