INTELLIGENT FAULT DETECTION AND LOCATION IN ELECTRICAL HIGH-VOLTAGE TRANSMISSION LINES

Abstract

Fault location in transmission lines is critical to ensure power systems' reliable and efficient operation. Accurate fault detection and localization are essential to minimize downtime, prevent cascading failures, and maintain the overall stability of the electrical grid. Over the years, various fault location methods have been proposed, ranging from traditional model-based approaches to more sophisticated artificial intelligence techniques. This research presents two fault location methodologies: the Atom search optimization metaheuristic approach (ASO) and machine learning (ML) with cubic spline models. We evaluate the performance of both approaches by considering different fault types, fault distances, and fault resistance. We analyze accuracy and computational efficiency. The findings reveal that the Metaheuristic Approach demonstrates robustness in fault detection and localization under diverse conditions but may suffer from higher computational overhead. In contrast, the hybridization of machine learning and metaheuristic exhibits promising potential in achieving real-time fault localization with improved accuracy.