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A Method for Fault Section Identification of Distribution Networks Based on Validation of Fault Indicators Using Artificial Neural Network

Author

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  • Myong-Soo Kim

    (Digital Solution Laboratory, KEPCO Research Institute, Daejeon 34056, Republic of Korea)

  • Jae-Guk An

    (Department of Electrical Engineering, Kyungnam University, Changwon 51767, Republic of Korea)

  • Yun-Sik Oh

    (Department of Electrical Engineering, Kyungnam University, Changwon 51767, Republic of Korea)

  • Seong-Il Lim

    (Department of Electrical Engineering, Kyungnam University, Changwon 51767, Republic of Korea)

  • Dong-Hee Kwak

    (Department of Electrical Engineering, Kyungnam University, Changwon 51767, Republic of Korea)

  • Jin-Uk Song

    (Department of Electrical Engineering, Kyungnam University, Changwon 51767, Republic of Korea)

Abstract

A fault section in Korean distribution networks is generally determined as a section between a switch with a fault indicator (FI) and a switch without an FI. However, the existing method cannot be applied to distribution networks with distributed generations (DGs) due to false FIs that are generated by fault currents flowing from the load side of a fault location. To identify the false FIs and make the existing method applicable, this paper proposes a method to determine the fault section by utilizing an artificial neural network (ANN) model for validating FIs, which is difficult to determine using mathematical equations. The proposed ANN model is built by training the relationship between the measured A, B, C, and N phase fault currents acquired by numerous simulations on a sample distribution system, and guarantees 100% FI validations for the test data. The proposed method can accurately distinguish genuine and false Fis by utilizing the ability of the ANN model, thereby enabling the conventional FI-based method to be applied to DG-connected distribution networks without any changes to the equipment and communication infrastructure. To verify the performance of the proposed method, various case studies considering real fault conditions are conducted under a Korean distribution network using MATLAB.

Suggested Citation

  • Myong-Soo Kim & Jae-Guk An & Yun-Sik Oh & Seong-Il Lim & Dong-Hee Kwak & Jin-Uk Song, 2023. "A Method for Fault Section Identification of Distribution Networks Based on Validation of Fault Indicators Using Artificial Neural Network," Energies, MDPI, vol. 16(14), pages 1-14, July.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:14:p:5397-:d:1194802
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    References listed on IDEAS

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    1. Muhammad Imam Ammarullah & Gatot Santoso & S. Sugiharto & Toto Supriyono & Dwi Basuki Wibowo & Ojo Kurdi & Mohammad Tauviqirrahman & J. Jamari, 2022. "Minimizing Risk of Failure from Ceramic-on-Ceramic Total Hip Prosthesis by Selecting Ceramic Materials Based on Tresca Stress," Sustainability, MDPI, vol. 14(20), pages 1-12, October.
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