Technology
Modern electrical grids are increasingly dynamic, with topologies that change due to distributed energy resources, load fluctuations, or fault conditions. Traditional control strategies often fall short in handling such complexity. This research explores a novel graph-based Deep Reinforcement Learning (DRL) framework to optimize electrical system performance across varying network topologies.