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A Parameter-Free Approach for Fault Section Detection on Distribution Networks Employing Gated Recurrent Unit

Author

Listed:
  • Mohammad Reza Shadi

    (Department of Electrical Engineering, Shahid Beheshti University, Tehran 1983969411, Iran)

  • Hamid Mirshekali

    (Clinical-Laboratory Center of Power System & Protection, Faculty of Intelligent Systems Engineering and Data Science, Persian Gulf University, Bushehr 75169113817, Iran)

  • Rahman Dashti

    (Clinical-Laboratory Center of Power System & Protection, Faculty of Intelligent Systems Engineering and Data Science, Persian Gulf University, Bushehr 75169113817, Iran)

  • Mohammad-Taghi Ameli

    (Department of Electrical Engineering, Shahid Beheshti University, Tehran 1983969411, Iran)

  • Hamid Reza Shaker

    (Center for Energy Informatics, University of Southern Denmark, DK-5230 Odense, Denmark)

Abstract

Faults in distribution networks can result in severe transients, equipment failure, and power outages. The quick and accurate detection of the faulty section enables the operator to avoid prolonged power outages and economic losses by quickly retrieving the network. However, the occurrence of diverse fault types with various resistances and locations and the highly non-linear nature of distribution networks make fault section detection challenging for numerous conventional techniques. This study presents a cutting-edge deep learning-based algorithm to distinguish fault sections in distribution networks to address these issues. The proposed gated recurrent unit model utilizes only two samples of the angle between the voltage and current on either side of the feeders, which record by smart feeder meters, to detect faulty sections in real time. When a network fault occurs, the protection relays trigger the trip command for the breakers. Immediately, the angle data are obtained from all smart feeder meters of the network, which comprises a pre-fault sample and a post-fault sample. The data are then employed as an input to the pre-trained gated recurrent unit model to determine the faulted line. The performance of this novel algorithm was validated through simulations of various fault types in the IEEE-33 bus system. The model recognizes the faulty section with competitive performance in terms of accuracy.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:19:p:6361-:d:650130
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    References listed on IDEAS

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    1. 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.
    2. Helbing, Georg & Ritter, Matthias, 2018. "Deep Learning for fault detection in wind turbines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 98(C), pages 189-198.
    3. Yangang Shi & Tao Zheng & Chang Yang, 2020. "Reflected Traveling Wave Based Single-Ended Fault Location in Distribution Networks," Energies, MDPI, vol. 13(15), pages 1-19, July.
    4. Shahriar Rahman Fahim & Subrata K. Sarker & S. M. Muyeen & Md. Rafiqul Islam Sheikh & Sajal K. Das, 2020. "Microgrid Fault Detection and Classification: Machine Learning Based Approach, Comparison, and Reviews," Energies, MDPI, vol. 13(13), pages 1-22, July.
    5. Seyyed Mohammad Nobakhti & Abbas Ketabi & Miadreza Shafie-khah, 2021. "A New Impedance-Based Main and Backup Protection Scheme for Active Distribution Lines in AC Microgrids," Energies, MDPI, vol. 14(2), pages 1-24, January.
    6. Wang, Shouxiang & Chen, Haiwen, 2019. "A novel deep learning method for the classification of power quality disturbances using deep convolutional neural network," Applied Energy, Elsevier, vol. 235(C), pages 1126-1140.
    7. Hamid Mirshekali & Rahman Dashti & Karsten Handrup & Hamid Reza Shaker, 2021. "Real Fault Location in a Distribution Network Using Smart Feeder Meter Data," Energies, MDPI, vol. 14(11), pages 1-16, June.
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    Cited by:

    1. Kimmo Kauhaniemi, 2023. "Protection and Communication Techniques in Modern Power Systems," Energies, MDPI, vol. 16(5), pages 1-2, February.

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