Chakraborty, Kabir and De, Sanchari and Saha, Tamanna and Nama, Purnima (2025) Fault location prediction under line-to-ground fault in transmission line using artificial neural network. World Journal of Advanced Engineering Technology and Sciences, 15 (2). pp. 857-866. ISSN 2582-8266
Abstract
The electrical power system occasionally suffers from failures, often caused by the faults occurring within the system. Accurate fault location prediction is important to ensure the reliable operation of the power system and to minimize the downtime during the occurrence of fault conditions. While traditional methods of fault location detection remain effective for specific scenarios, Artificial Neural Network (ANN) provide a more versatile, efficient, and cost-effective approach to fault location detection. This study focuses on predicting fault positions under line-to-ground (L-G) fault using ANN.
Item Type: | Article |
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Official URL: | https://doi.org/10.30574/wjaets.2025.15.2.0552 |
Uncontrolled Keywords: | Power System Analysis; L-G Fault; Artificial Neural Network; Artificial Neural Network |
Date Deposited: | 04 Aug 2025 16:34 |
Related URLs: | |
URI: | https://eprint.scholarsrepository.com/id/eprint/3612 |