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A Comparison of Impedance-Based Fault Location Methods for Power Underground Distribution Systems

Author

Listed:
  • Enrique Personal

    (Department of Electronic Technology, University of Seville, 41011 Seville, Spain)

  • Antonio García

    (Department of Electronic Technology, University of Seville, 41011 Seville, Spain)

  • Antonio Parejo

    (Department of Electronic Technology, University of Seville, 41011 Seville, Spain)

  • Diego Francisco Larios

    (Department of Electronic Technology, University of Seville, 41011 Seville, Spain)

  • Félix Biscarri

    (Department of Electronic Technology, University of Seville, 41011 Seville, Spain)

  • Carlos León

    (Department of Electronic Technology, University of Seville, 41011 Seville, Spain)

Abstract

In the last few decades, the Smart Grid paradigm presence has increased within power systems. These new kinds of networks demand new Operations and Planning approaches, following improvements in the quality of service. In this sense, the role of the Distribution Management System, through its Outage Management System, is essential to guarantee the network reliability. This system is responsible for minimizing the consequences arising from a fault event (or network failure). Obviously, knowing where the fault appears is critical for a good reaction of this system. Therefore, several fault location techniques have been proposed. However, most of them provide individual results, associated with specific testbeds, which make the comparison between them difficult. Due to this, a review of fault location methods has been done in this paper, analyzing them for their use on underground distribution lines. Specifically, this study is focused on an impedance-based method because their requirements are in line with the typical instrumentation deployed in distribution networks. This work is completed with an exhaustive analysis of these methods over a PSCAD TM X4 implementation of the standard IEEE Node Test Feeder, which truly allows us to consistently compare the results of these location methods and to determine the advantages and drawbacks of each of them.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:9:y:2016:i:12:p:1022-:d:84575
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    References listed on IDEAS

    as
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    2. Luca Ardito & Giuseppe Procaccianti & Giuseppe Menga & Maurizio Morisio, 2013. "Smart Grid Technologies in Europe: An Overview," Energies, MDPI, vol. 6(1), pages 1-31, January.
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    5. Personal, Enrique & Guerrero, Juan Ignacio & Garcia, Antonio & Peña, Manuel & Leon, Carlos, 2014. "Key performance indicators: A useful tool to assess Smart Grid goals," Energy, Elsevier, vol. 76(C), pages 976-988.
    6. Lucas Cuadra & Sancho Salcedo-Sanz & Javier Del Ser & Silvia Jiménez-Fernández & Zong Woo Geem, 2015. "A Critical Review of Robustness in Power Grids Using Complex Networks Concepts," Energies, MDPI, vol. 8(9), pages 1-55, August.
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    Citations

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    Cited by:

    1. Dazhi Wang & Yi Ning & Cuiling Zhang, 2018. "An Effective Ground Fault Location Scheme Using Unsynchronized Data for Multi-Terminal Lines," Energies, MDPI, vol. 11(11), pages 1-16, October.
    2. Tao Tang & Chun Huang & Zhenxing Li & Xiuguang Yuan, 2019. "Identifying Faulty Feeder for Single-Phase High Impedance Fault in Resonant Grounding Distribution System," Energies, MDPI, vol. 12(4), pages 1-15, February.
    3. Yi Ning & Dazhi Wang & Yunlu Li & Haixin Zhang, 2018. "Location of Faulty Section and Faults in Hybrid Multi-Terminal Lines Based on Traveling Wave Methods," Energies, MDPI, vol. 11(5), pages 1-18, May.
    4. 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.
    5. Ehsan Gord & Rahman Dashti & Mojtaba Najafi & Hamid Reza Shaker, 2019. "Real Fault Section Estimation in Electrical Distribution Networks Based on the Fault Frequency Component Analysis," Energies, MDPI, vol. 12(6), pages 1-29, March.
    6. Hamid Mirshekali & Athila Q. Santos & Hamid Reza Shaker, 2023. "A Survey of Time-Series Prediction for Digitally Enabled Maintenance of Electrical Grids," Energies, MDPI, vol. 16(17), pages 1-29, August.
    7. Danilo Pinto Moreira de Souza & Eliane Da Silva Christo & Aryfrance Rocha Almeida, 2017. "Location of Faults in Power Transmission Lines Using the ARIMA Method," Energies, MDPI, vol. 10(10), pages 1-12, October.
    8. Stephen Robson & Abderrahmane Haddad & Huw Griffiths, 2018. "Traveling Wave Fault Location Using Layer Peeling," Energies, MDPI, vol. 12(1), pages 1-23, December.
    9. Ednardo Rocha & Max Pimentel Filho & Melinda Cruz & Marcos Almeida & Manoel Medeiros Júnior, 2020. "A New Linear State Estimator for Fault Location in Distribution Systems Based on Backward-Forward Currents Sweep," Energies, MDPI, vol. 13(11), pages 1-23, May.
    10. Kongming Sun & Qing Chen & Pu Zhao, 2017. "Automatic Faulted Feeder Section Location and Isolation Method for Power Distribution Systems Considering the Change of Topology," Energies, MDPI, vol. 10(8), pages 1-22, July.

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