Intelligent Control of Power Converters using Reinforcement Learning

Power converter control techniques must evolve to accommodate power system complexity and renewable energy integration. Traditional control methods may be difficult to adapt to these dynamic and uncertain systems. Reinforcement learning (RL) can build optimal control policies in complex and unpredictable situations, making it a promising intelligent control solution in this scenario. This research examines how reinforcement learning can control power converters in energy systems. Advanced reinforcement learning methods, notably deep reinforcement learning, are used to improve power converter efficiency in varied operational scenarios. This research examines the integration of reinforcement learning (RL)-based controllers into power converter systems, focusing on their autonomous learning and adaptation. The explored method uses Reinforcement Learning (RL) flexibility to address power system time-varying conditions, non-linearities, and uncertainties. This research considers practical concerns such RL-based control's effects on real-time execution, processing requirements, and safety. The report concludes with suggestions for future research including expanding RL-based intelligent control systems to power system applications. Reinforcement learning keywords include stability, efficiency, reliability, renewable energy, deep reinforcement learning, power converters, intelligent control, and energy systems.