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

Zhihong Ge, Xingshuo Li, Fei Xu, Haimeng Wu, Ruichi Wang, Shuye Ding

Research output: Contribution to journalArticlepeer-review


The mismatch problem is commonly happened in photovoltaic (PV) systems due to partial shading conditions(PSCs). Distributed maximum power point tracking
(DMPPT) architectures can be used to solve such problem. Reinforcement learning (RL) method which is one of the advanced artificial intelligence(AI) methods is proposed to improve the tracking speed. However, the drawbacks such as lack of limited adaptability and exploration-exploitation trade-off theory make the RL method low in efficiency. Therefore, this article combines the Beta method and ε - greedy algorithm with the RL method to address this problem. The simulation and experimental tests have been carried out. The result shows the efficiency of the proposed RL method is up to 96.85%, which verifies the superiority of the proposed scheme.
Original languageEnglish
Pages (from-to)1-12
Number of pages12
JournalIEEE Journal of Emerging and Selected Topics in Power Electronics
Early online date13 Nov 2023
Publication statusE-pub ahead of print - 13 Nov 2023

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