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Real Fault Section Estimation in Electrical Distribution Networks Based on the Fault Frequency Component Analysis

Author

Listed:
  • Ehsan Gord

    (Department of Electrical Engineering, Bushehr Branch, Islamic Azad University, Bushehr 7515895496, Iran)

  • Rahman Dashti

    (Power System & Protection Lab., Engineering Faculty, Persian Gulf University, Bushehr 7516913817, Iran)

  • Mojtaba Najafi

    (Department of Electrical Engineering, Bushehr Branch, Islamic Azad University, Bushehr 7515895496, Iran)

  • Hamid Reza Shaker

    (Center for Energy Informatics, The Maersk Mc Kinney Moller Institute, University of Southern Denmark, 5230 Odense, Denmark)

Abstract

Fault location in electrical energy distribution networks is an important task, as faults in distribution grids are among the main causes of electricity supply disruption. Fault location in the distribution systems, however, is a challenging task because of the topology of the distribution networks, as well as the main and side branches. Therefore, it is necessary to address these challenges through an intelligent approach to fault location. In this paper, fault location in electric energy distribution networks is addressed considering the changes in fault distance and fault resistance in the presence of different fault types. A new method for fault location is developed for conditions where the minimum information is available and only information at the beginning of the feeder is used. This facilitates wide adoption of the technique as it does not require significant investments in instrumentation and measurement. The proposed intelligent method is based on the impedance and transient state estimation. This technique employs a specific impedance analysis for determining possible fault locations considering the unbalanced performance of distribution systems, distances, and different fault resistances. To determine the real faulty section, real fault frequency component analysis and the simulated faults at possible fault locations are used. At this stage of the process, it is possible to eliminate multiple estimations with the help of comparison and identification of the similarities. Therefore, a real faulty section is determined. It is observed that some conditions of electric energy distribution networks affect the accuracy and performance of the proposed method significantly; thus, a detailed investigation is conducted to neutralize these conditions. Simulation results and calculations based on MATLAB along with a practical test of the proposed method in power network simulator confirm a satisfactory performance.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:6:p:1145-:d:216780
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    References listed on IDEAS

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    1. Zidan, Aboelsood & El-Saadany, Ehab F., 2013. "Incorporating load variation and variable wind generation in service restoration plans for distribution systems," Energy, Elsevier, vol. 57(C), pages 682-691.
    2. Zidan, Aboelsood & El-Saadany, E.F., 2015. "Incorporating customers' reliability requirements and interruption characteristics in service restoration plans for distribution systems," Energy, Elsevier, vol. 87(C), pages 192-200.
    3. Dashti, Reza & Yousefi, Shaghayegh & Parsa Moghaddam, Mohsen, 2013. "Comprehensive efficiency evaluation model for electrical distribution system considering social and urban factors," Energy, Elsevier, vol. 60(C), pages 53-61.
    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. Esmaeeli, M. & Kazemi, A. & Shayanfar, H.A. & Haghifam, M.-R., 2015. "Multistage distribution substations planning considering reliability and growth of energy demand," Energy, Elsevier, vol. 84(C), pages 357-364.
    6. Daisy, Mohammad & Dashti, Rahman, 2016. "Single phase fault location in electrical distribution feeder using hybrid method," Energy, Elsevier, vol. 103(C), pages 356-368.
    7. Dashti, Rahman & Ghasemi, Mohsen & Daisy, Mohammad, 2018. "Fault location in power distribution network with presence of distributed generation resources using impedance based method and applying π line model," Energy, Elsevier, vol. 159(C), pages 344-360.
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    Cited by:

    1. 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.
    2. Mohammad Reza Shadi & Hamid Mirshekali & Rahman Dashti & Mohammad-Taghi Ameli & Hamid Reza Shaker, 2021. "A Parameter-Free Approach for Fault Section Detection on Distribution Networks Employing Gated Recurrent Unit," Energies, MDPI, vol. 14(19), pages 1-15, October.

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