Enhancing Transmission Line Fault Classification and Prediction of Fault Location Using ML and DL Techniques

ABSTRACT

Accurate fault classification and location in transmission lines plays a critical role in ensuring the reliability, stability and efficiency of the power system. Accurate fault detection and classification is key to safety, enabling brisk resolution of problems and mitigating electrical power supply disturbances. Traditional methods for fault type classification (FTC) and location prediction (LP) struggle to handle real‐world fault situations, facing challenges like delays, limited capacity and vulnerability to single‐point failures. This study develops and compares various machine learning (ML) and deep learning (DL) algorithms along with artificial neural network (ANN) to classify transmission line faults—LG, LLG, LL and three‐phase faults—and predict fault locations. Fault data was generated using a MATLAB Simulink model incorporating phase voltages, currents, phase angles, zero‐sequence, and negative‐sequence components from both sending and receiving ends. The method achieved 99.69% and 99.78% accuracy using random forest and CatBoost for FTC, and of 1.00, MAE of 0.0080, and RMSE of 0.0729 for LP. Noise robustness was validated across SNR values (0–50 dB), achieving 90.53% FTC accuracy at 50 dB by CatBoost and of 0.9999 by random forest in LP. The study demonstrates exceptional performance of ML and DL models over traditional methods.