Advancements in grid resilience: Recent innovations in AI-driven solutions

In response to increasing complexities in modern power grids posed by the integration of smart grids, smart meters, microgrids, Renewable Energy Resources (RERs), and Distributed Energy Resources (DERs), there has been a growing emphasis on leveraging Artificial Intelligence (AI) to enhance the resilience of power grids. In recent years, there has been several advancements in employing various AI methodologies such as Machine Learning (ML), Deep Learning (DL), and Reinforcement Learning (RL) for dealing with the challenges caused by climate change, cybersecurity threats, and evolving energy demands. The applications of AI can span across different aspects of grid resilience, including fault detection and diagnosis, predictive maintenance, optimal resource allocation, demand-side management, and cyber resilience. This review provides an overview of the recent advancements in implementing AI to enhance grid resilience. It discusses the challenges and future directions for research and development in this rapidly evolving field. • This review explores AI advancements for enhancing the resilience of power grids. • AI addresses challenges in power grids, including climate change and energy demands. • Applications include fault detection, resource allocation, and cybersecurity. • Future research focuses on overcoming challenges and advancing AI for grid resilience. • AI enhances power grids' adaptability to evolving energy needs and environmental shifts.