Abstract
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.
(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 language | English |
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Pages (from-to) | 167-178 |
Number of pages | 12 |
Journal | IEEE Journal of Emerging and Selected Topics in Power Electronics |
Volume | 5 |
Issue number | 1 |
Early online date | 13 Nov 2023 |
DOIs | |
Publication status | Published - Jan 2024 |
Keywords
- Distributed maximum power point tracking (DMPPT
- reinforcement learning(RL)
- partial shading conditions (PSCs)
- PV systems