Deep Learning‐Based Fault Zone Classification in Transmission Lines Using Impedance Trajectories

ABSTRACT Energy transmission lines are critical for reliable electricity delivery, but faults in these lines can disrupt power systems, leading to instability and outages. Rapid fault detection, accurate classification, and timely isolation are essential to maintain grid resilience. Traditional distance relays are widely used for fault detection. However, due to dynamic variations in system conditions and external disturbances, they face limitations in complex scenarios. This situation highlights the need for more advanced and precise fault detection methods. In this study, parametric analyses were conducted on a 400 kV medium‐length transmission line, and various fault scenarios were created using system characteristic data with various program software to generate a dataset. The performance of deep learning techniques in fault detection was comparatively analysed based on the obtained datasets. Notably, the proposed hybrid deep learning algorithm outperformed other classification algorithms in predicting critical information such as fault type and zone detection. The proposed hybrid framework demonstrates strong predictive performance, ensuring high accuracy in fault type classification, 99.994%, and zone localisation, 99.981%. By systematically integrating conventional protection schemes with advanced computational methodologies, the approach is anticipated to significantly enhance the reliability and operational efficiency of fault management in high‐voltage transmission networks.