Adaptive Synchronverter Algorithm Using Q-Learning for Microgrid Stability Control

The integration of renewable energy sources into microgrids often leads to stability challenges, primarily due to the absence of inertia typically provided by conventional generators. This lack of inertia can be mitigated through the use of synchronverters, which emulate the dynamic characteristics of synchronous generators. However, traditional synchronverter control systems rely on fixed values for virtual inertia and voltage control gain, which limits their adaptability to changing grid conditions. To address this limitation, this research proposes a novel control strategy for synchronverters based on reinforcement learning (RL), enabling adaptive adjustment of both virtual inertia and voltage control gain. The Q-learning algorithm is selected for this purpose, as it is capable of handling continuous observation spaces and discrete action spaces, making it well-suited for optimization in complex systems. The proposed Q-learning-based synchronverter control algorithm adjusts the inverter output dynamically, ensuring both frequency and voltage stability within the microgrid. The results demonstrate that the Q-learning-based synchronverter, with its adaptive control parameters, achieves better stability performance compared to conventional synchronverters, offering enhanced flexibility and robustness in maintaining microgrid stability. © 2013 IEEE.