Future Potential of AI-Based Fault Location Estimators in Modern Power Transmission Systems

The accurate identification of fault locations in power transmission networks is critical for ensuring system reliability and reducing downtime. Traditional fault location methods, such as impedance-based techniques, have been widely used, but they often suffer from limitations due to system complexity and changing network conditions. Recently, artificial intelligence (AI)-based approaches, including machine learning (ML) and deep learning (DL) models have emerged as promising alternatives for improving fault location accuracy. This paper reviews various traditional and AI-based fault location methods, highlighting their advantages, challenges, and applications in modern power systems. The study provides insights into the effectiveness of these techniques and suggests future research directions for enhancing fault location accuracy in transmission networks.