IDENTIFICATION OF LINE-TO-LINE-TO-GROUND FAULT LOCATION IN ELECTRICAL POWER NETWORK USING ARTIFICIAL NEURAL NETWORK

Author(s)	Prof. Dr. Kabir Chakraborty, Mr. Ankit Roy Sarkar, Ms. Payel Debbarma, Mr. Arijit Banik
Country	India
Abstract	The identifying of fault location line-to-line-to-ground (LLG) fault in electrical power network is critical for ensuring the system reliability, safety, and reducing the downtime, and minimizing outage times, and optimizing maintenance to efforts. Various faults that can occur, with line-to-line-to-ground (LLG) faults are complex and less frequent but can cause significant damage if not detected and resolved promptly. The study focuses on identifying the fault location of LLG fault in electrical power network. The ANN model uses voltage and current readings from both ends of the line to predicting the under LLG of fault location. The ANN model it was trained and tested using simulated fault data generated by a power system under various fault location. The result demonstrated the effectiveness of artificial neural Network in accurately identifying of the fault location with line-to-line-to-ground (LLG) faults in electrical power network.
Keywords	Artificial Neural Network, Overhead Transmission Lines, Fault Detection, Distance Protection, Power System Stability.
Field	Engineering
Published In	Volume 7, Issue 2, March-April 2025
Published On	2025-04-30
DOI	https://doi.org/10.36948/ijfmr.2025.v07i02.43007
Short DOI	https://doi.org/g9g729

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