Self-learning Maximum Power Point Tracking with Environmental Adaptation for Photovoltaic Power Systems

2nd International Symposium on Resource Exploration and Environmental Science

生态环境科学

Song, Xinming^1 ; Xie, Congzhen^2 ; Xia, Yunfeng^1 ; Hu, Jianghua^1 ; Liu, Zhijian^2

Dongguan Power Supply Bureau of Guangdong Power Grid Co. Ltd, Dongguan, Guangdong, China^1

School of Electric Power, South China University of Technology, Guangzhou, China^2

关键词: Atmospheric conditions;    Charge and discharge;    Control circuits;    Environmental adaptation;    Hardware experiment;    Maximum Power Point Tracking;    Photovoltaic power systems;    Photovoltaic systems;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/170/4/042097/pdf
DOI  :  10.1088/1755-1315/170/4/042097

学科分类：环境科学（综合）

来源: IOP

【 摘 要 】

A novel self-learning method is proposed for maximum power point tracking (MPPT) in photovoltaic (PV) system. A simple control circuit is designed to make its implementation effective, in which the super capacitor is used to charge and discharge to achieve fast control. Furthermore, the proposed method is implemented with hardware experiments and MATLAB/Simulink for simulations. The experimental results verify the feasibility of this method for MPPT, while simulations in MATLAB/Simulink indicate that the proposed PV system can react quickly within milliseconds under enormous changes of atmospheric conditions.