期刊
IEEE TRANSACTIONS ON POWER ELECTRONICS
卷 36, 期 12, 页码 13826-13838出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TPEL.2021.3089707
关键词
Control engineering; maximum power point tracking (MPPT); optimization; photovoltaic power system; reinforcement learning (RL); self-tuning
资金
- National Natural Science Foundation of China [51977112]
- Natural Science Research Project of Jiangsu Higher Education Institutions [20KJB470020]
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.
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据