Self-Tuning MPPT Scheme Based on Reinforcement Learning and Beta Parameter in Photovoltaic Power Systems

Maximum power point tracking (MPPT) is required in PV power systems for the highest solar energy harvest. This article proposes a self-tuning scheme to improve the MPPT performance in terms of high accuracy and speed. The scheme adopts the reinforcement learning (RL) and Beta parameter for the highest MPPT performance. The tracking speed and accuracy are significantly improved since the RL algorithm is enhanced for high convergence speed, meanwhile, the guiding variable beta is introduced to constrain the exploration space. Simulation and experimental test are applied to validate the superior performance of the proposed solution following the EN50530 dynamic test procedure.

Automated summary

The article proposes a self-tuning scheme to improve the performance of maximum power point tracking (MPPT) in PV power systems, achieving high accuracy and speed through reinforcement learning algorithm and Beta parameter. By optimizing the RL algorithm and guiding variable Beta, the tracking speed and accuracy are significantly improved, and simulation and experimental tests confirm the superior performance of the proposed solution.