self-tuning MPPT scheme based on reinforcement learning and Beta parameter in photovoltaic power systems

Lin, Dingyi, Li, Xingshuo, Ding, Shuye, Wen, Huiqing, Du, Yang, and Xiao, Weidong (2021) self-tuning MPPT scheme based on reinforcement learning and Beta parameter in photovoltaic power systems. IEEE Transactions on Power Electronics, 36 (12). pp. 13826-13838.

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Abstract

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 β 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.

Item ID: 69953
Item Type: Article (Research - C1)
ISSN: 1941-0107
Keywords: Control engineering, maximum power point tracking (MPPT), optimization, photovoltaic power system, reinforcement learning (RL), self-tuning
Copyright Information: © 2021 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See https://www.ieee.org/publications/rights/index.html for more information.
Date Deposited: 14 Apr 2022 05:06
FoR Codes: 40 ENGINEERING > 4008 Electrical engineering > 400808 Photovoltaic power systems @ 60%
46 INFORMATION AND COMPUTING SCIENCES > 4611 Machine learning > 461105 Reinforcement learning @ 40%
SEO Codes: 17 ENERGY > 1708 Renewable energy > 170804 Solar-photovoltaic energy @ 100%
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