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

Myong-Soo Kim, Jae-Guk An, Yun-Sik Oh, Seong-Il Lim, Dong-Hee Kwak and Jin-Uk Song ()
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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

Energies, 2023, vol. 16, issue 14, 1-14

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.

Keywords: artificial neural network; distribution automation system; distribution network; fault indicator; fault section identification (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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