Smart Grid Stability Prediction for Efficient Power Management using Machine Learning

In advancing power grid management, integrating artificial intelligence (AI) has emerged as a pivotal approach, particularly in smart grids. The modernization of power grids into intelligent, interconnected systems has introduced challenges in terms of stability prediction and efficient power management. Traditional methodologies often do not cope adequately with the intricacies associated with bidirectional communication and increased data volumes. This paper addresses the imperative need for enhancing smart grid stability through efficient power management by employing advanced algorithms and artificial intelligence (AI) techniques. Our study evaluates five algorithms for stability prediction. Leveraging bidirectional communication between the grid and consumers, machine learning algorithms, including deep neural networks, demonstrate superior performance in forecasting energy needs. We further explored AI-based in power flow studies, topology detection, judge/task assignment state estimation/load monitoring, Volt/VAR optimization, and line parameter tuning. The primary aim of this research work is the optimization of several aspects of smart grid functionality through the application of Bayesian networks, Deep Reinforcement Learning, Transfer Learning, and Graph Neural Networks. Our study evaluates five algorithms for stability prediction in smart grids, revealing varying degrees of accuracy and efficiency. These findings provide crucial insights for selecting optimal algorithms, advancing the reliability and effectiveness of machine learning in enhancing power management within smart grid systems.