Challenges and solutions of Artificial Intelligence-based fault location methods in power system lines

Abstract

The accurate and efficient location of faults in power system lines is crucial for ensuring reliable and uninterrupted power supply. In recent years, Artificial Intelligence (AI) has been increasingly used in fault location methods, promising to improve the accuracy and efficiency of fault location. However, AI-based fault location methods also face challenges such as data quality, interpretability, and model robustness. Review method: This paper presents a review of the challenges and solutions of AI-based fault location methods in power system lines. The review is based on a comprehensive analysis of existing literature and research studies, focusing on the challenges associated with AI-based fault location methods and the solutions proposed to address these challenges. Content: The paper discusses the challenges associated with AI-based fault location methods in power system lines, including data quality, interpretability, and model robustness. The review presents several solutions to address these challenges, including data preprocessing techniques to improve data quality, explainable AI methods to enhance interpretability, and robustness validation techniques to improve model robustness. The accurate and efficient location of faults in power system lines is crucial for ensuring reliable and uninterrupted power supply. AI-based fault location methods have the potential to improve the accuracy and efficiency of fault location. However, these methods also face challenges such as data quality, interpretability, and model robustness. Addressing these challenges through techniques such as data preprocessing, explainable AI, and robustness validation can help to improve the accuracy and reliability of AI-based fault location methods.