Research on MPPT control of photovoltaic power generation under complex lighting conditions based on RBF neural network

Cheng Shen; Xiao-qing Xu; Xin Liu; Ying Liu

doi:10.1117/12.3004432

25 September 2023 Research on MPPT control of photovoltaic power generation under complex lighting conditions based on RBF neural network

Cheng Shen, Xiao-qing Xu, Xin Liu, Ying Liu

Author Affiliations +

Proceedings Volume 12788, Second International Conference on Energy, Power, and Electrical Technology (ICEPET 2023); 127881S (2023) https://doi.org/10.1117/12.3004432
Event: Second International Conference on Energy, Power, and Electrical Technology (ICEPET 2023), 2023, Kuala Lumpur, Malaysia

Abstract

The comprehensive efficiency of photovoltaic power generation system is only a little more than 10% while the efficiency of inverter is considered, in order to maximize the use of solar energy, it is especially necessary to adjust the working point of photovoltaic array in real time, so that it always works near the maximum power point. Photovoltaic power generation system is a very nonlinear system, its mathematical model is complex and it is difficult to get its accurate model. In this paper, to avoid the internal complexity of photovoltaic power generation module, the RBF neural network identification technology is used to establish the nonlinear MPPT control model of photovoltaic power generation module, and the control effect of the MPPT controller is verified by simulation. It is compared with the traditional perturbation observation control method. The simulation results show that it is feasible to use RBF neural network to establish the nonlinear model of photovoltaic power generation module under complex lighting conditions, and the designed RBF neural network MPPT controller is effective, which can be used to predict the MPPT response of photovoltaic power generation module online, and its performance is much better than that of traditional controllers based on disturbance observation method.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Cheng Shen, Xiao-qing Xu, Xin Liu, and Ying Liu "Research on MPPT control of photovoltaic power generation under complex lighting conditions based on RBF neural network", Proc. SPIE 12788, Second International Conference on Energy, Power, and Electrical Technology (ICEPET 2023), 127881S (25 September 2023); https://doi.org/10.1117/12.3004432

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KEYWORDS

Solar cells

Neural networks

Photovoltaics

Mathematical modeling

Shadows

Complex systems

Detection and tracking algorithms

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