Deep Learning‐Based Fault Classification in Extra High Voltage Transmission Lines: A Comparative Study Using Simulated and Real‐Time Sequential Data

ABSTRACT Prompt and accurate fault detection in extra high voltage transmission lines is required for guaranteeing the steadiness of power system. This study describes the performance of BiLSTM, GRU, and TCN as deep learning models for the detection and classification of faults in transmission lines through synthetic and real‐time sequential datasets in 500 kV transmission line between Jamshoro and Karachi (NKI), in Sindh, Pakistan. Testing models' performance on simulated faults versus real fault events, the study concludes a major space and suggests insights for their practical applicability. The results show that deep learning models can reach vast level of accuracy in classifying different faults in transmission lines. This study forms the basis for exploiting modern fault detection practices in operating grids to improve their dependability and flexibility. The results revealed an accuracy of 98.31%, achieved by the BiLSTM, 94.27% for GRU and TCN as 99.8% through simulated data set, whereas using real‐time fault data BiLSTM scored 62.05% accuracy, while GRU accuracy score achieved 96.43%, and TCN attained 100% accuracy. The results demonstrate that the deep learning models used in this study work well analyzing time series data by achieving high fault accuracy for fault classification in transmission lines. In general, the study was conducted to identify the best model in managing the fault over extra high voltage transmission lines under different conditions.