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Fault location and detection techniques in power distribution systems with distributed generation: A review

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  • Gururajapathy, S.S.
  • Mokhlis, H.
  • Illias, H.A.

Abstract

Distribution systems are continuously exposed to fault occurrences due to various reasons, such as lightning strike, failure of power system components due to aging of equipment and human errors. These phenomena affect the system reliability and results in expensive repairs, lost of productivity and power loss to customers. Since fault is unpredictable, a fast fault location and isolation is required to minimize the impact of fault in distribution systems. Therefore, many methods have been developed since the past to locate and detect faults in distribution systems with distributed generation. The methods can be divided into two categories, conventional and artificial intelligence techniques. Conventional techniques include travelling wave method and impedance based method while artificial intelligence techniques include Artificial Neural Network (ANN), Support Vector Machine (SVM), Fuzzy Logic, Genetic Algorithm (GA) and matching approach. However, fault location using intelligent methods are challenging since they require training data for processing and are time consuming. In this paper, most of the techniques that have been developed since the past and commonly used to locate and detect faults in distribution systems with distributed generation are reviewed. Research works in fault location area, the working principles, advantages and disadvantages of past works related to each fault location technique are highlighted in this paper. Hence, from this review, the opportunities in fault location research area in power distribution system can be explored further.

Suggested Citation

  • Gururajapathy, S.S. & Mokhlis, H. & Illias, H.A., 2017. "Fault location and detection techniques in power distribution systems with distributed generation: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 949-958.
    • Handle: RePEc:eee:rensus:v:74:y:2017:i:c:p:949-958
    DOI: 10.1016/j.rser.2017.03.021
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    Citations

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    Cited by:

    1. Jinrui Tang & Binyu Xiong & Chen Yang & Cuilan Tang & Yang Li & Guoxing Su & Xinhao Bian, 2019. "Development of an Integrated Power Distribution System Laboratory Platform Using Modular Miniature Physical Elements: A Case Study of Fault Location," Energies, MDPI, vol. 12(19), pages 1-19, October.
    2. Saeid Khavari & Rahman Dashti & Hamid Reza Shaker & Athila Santos, 2020. "High Impedance Fault Detection and Location in Combined Overhead Line and Underground Cable Distribution Networks Equipped with Data Loggers," Energies, MDPI, vol. 13(9), pages 1-15, May.
    3. Jia, Ke & Gu, Chenjie & Li, Lun & Xuan, Zhengwen & Bi, Tianshu & Thomas, David, 2018. "Sparse voltage amplitude measurement based fault location in large-scale photovoltaic power plants," Applied Energy, Elsevier, vol. 211(C), pages 568-581.
    4. Jun Yin Lee & Renuga Verayiah & Kam Hoe Ong & Agileswari K. Ramasamy & Marayati Binti Marsadek, 2020. "Distributed Generation: A Review on Current Energy Status, Grid-Interconnected PQ Issues, and Implementation Constraints of DG in Malaysia," Energies, MDPI, vol. 13(24), pages 1-40, December.
    5. Pascal Hategekimana & Adria Junyent Ferre & Joan Marc Rodriguez Bernuz & Etienne Ntagwirumugara, 2022. "Fault Detecting and Isolating Schemes in a Low-Voltage DC Microgrid Network from a Remote Village," Energies, MDPI, vol. 15(12), pages 1-16, June.
    6. Zhidi Lin & Dongliang Duan & Qi Yang & Xuemin Hong & Xiang Cheng & Liuqing Yang & Shuguang Cui, 2020. "Data-Driven Fault Localization in Distribution Systems with Distributed Energy Resources," Energies, MDPI, vol. 13(1), pages 1-16, January.
    7. Moamin A. Mahmoud & Naziffa Raha Md Nasir & Mathuri Gurunathan & Preveena Raj & Salama A. Mostafa, 2021. "The Current State of the Art in Research on Predictive Maintenance in Smart Grid Distribution Network: Fault’s Types, Causes, and Prediction Methods—A Systematic Review," Energies, MDPI, vol. 14(16), pages 1-27, August.
    8. Miguel Jiménez-Aparicio & Matthew J. Reno & Felipe Wilches-Bernal, 2022. "Traveling Wave Energy Analysis of Faults on Power Distribution Systems," Energies, MDPI, vol. 15(8), pages 1-28, April.
    9. Masoud Ahmadipour & Hashim Hizam & Mohammad Lutfi Othman & Mohd Amran Mohd Radzi & Nikta Chireh, 2019. "A Fast Fault Identification in a Grid-Connected Photovoltaic System Using Wavelet Multi-Resolution Singular Spectrum Entropy and Support Vector Machine," Energies, MDPI, vol. 12(13), pages 1-18, June.
    10. Sellak, Hamza & Ouhbi, Brahim & Frikh, Bouchra & Palomares, Iván, 2017. "Towards next-generation energy planning decision-making: An expert-based framework for intelligent decision support," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 1544-1577.
    11. Hernandez-Matheus, Alejandro & Löschenbrand, Markus & Berg, Kjersti & Fuchs, Ida & Aragüés-Peñalba, Mònica & Bullich-Massagué, Eduard & Sumper, Andreas, 2022. "A systematic review of machine learning techniques related to local energy communities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 170(C).
    12. Saidatul Habsah Asman & Nur Fadilah Ab Aziz & Ungku Anisa Ungku Amirulddin & Mohd Zainal Abidin Ab Kadir, 2021. "Transient Fault Detection and Location in Power Distribution Network: A Review of Current Practices and Challenges in Malaysia," Energies, MDPI, vol. 14(11), pages 1-37, May.

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