Classification and Prediction of Faults in Power Grids Using Machine Learning Algorithms

The reliability and stability of power grids are critical for maintaining continuous electricity supply to consumers. Faults in power grids can lead to significant economic and social impacts, making their early detection and classification essential. This paper presents a comprehensive study on the application of machine learning algorithms for fault classification and prediction in power grids. Utilizing real-world data collected from various sensors and monitoring devices, we have evaluated the performance of several machine learning models, including Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Naïve Bayes (NB), XGboost and Random Forest (RF). Our findings indicate that the SVM algorithms outperformed other methods with an accuracy of 86% in classifying faults. This study also explores the potential of machine learning in predicting faults, contributing to proactive maintenance strategies and enhancing the resilience of power grids. The implications of this research are significant for the development of intelligent fault detection and prediction systems, ultimately improving operational efficiency and customer satisfaction.