An Improved Distributed Maximum Power Point Tracking Technique in Photovoltaic Systems Based on Reinforcement Learning Algorithm

The mismatch problem is commonly happened in photovoltaic systems due to partial shading conditions. Distributed maximum power point tracking architectures can be used to solve such problem. Reinforcement learning (RL) method, which is one of the advanced artificial intelligence methods is proposed to improve the tracking speed. However, the drawbacks, such as the lack of limited adaptability and exploration–exploitation tradeoff theory, make the RL method low in efficiency. Therefore, this article combines the Beta method and
$\varepsilon$
–greedy algorithm with the RL method to address this problem. The simulation and experimental tests have been carried out and the result shows the efficiency of the proposed RL method is up to 96.85%, which verifies the superiority of the proposed scheme.