Reinforcement Learning Based Control of Grid-Connected PUC5 Inverter

In this paper, a Reinforcement Learning controller (RLC) is designed and implemented on a 5-level Packed U-Cell (PUC5) grid-connected inverter to control the injected current flowing into the electric network. The RL agent is trained using a Proportional-Integral (PI) reward function to optimize its control strategy. Moreover, the voltage balancing of the auxiliary capacitor in PUC5 is separated from the RL controller and integrated into the switching algorithm to reduce the training burden. This modification reduces the observation inputs required for RL training, significantly shorten the training time. Simulation studies conducted in Matlab/Simulink evaluate the performance of the proposed RL controller, demonstrating robust dynamic response and accurate tracking of reference signals across different operational conditions.