Machine Learning Approaches to Improve Fault Location and Diagnosis in Smart Grids: Current Trends and Future Directions

This review paper explores the use of machine learning (ML) in enhancing fault location and diagnosis within smart grids. It highlights the transition from traditional electricity distribution to advanced systems leveraging real-time data and communication networks. The paper examines various ML algorithms, such as neural networks, decision trees, and support vector machines, and their application in accurately detecting, locating, and diagnosing grid faults. It underscores the significance of ML in improving grid reliability and efficiency, particularly in integrating intermittent renewable energy sources. The review also discusses challenges related to data quality, model interpretability, and system integration, offering insights into overcoming these obstacles. Additionally, it looks at future directions for ML in smart grids, considering ongoing technological advancements. The paper concludes with strategic recommendations for researchers and practitioners in the field, emphasizing the role of ML in driving a more sustainable, efficient, and reliable energy future.