Title: Maximum power tracking method for roof solar cells in intelligent buildings based on particle swarm optimisation

Authors: Yingjie Wang; Caihong Chu

Addresses: School of Artificial Intelligence, Zhengzhou Railway Vocational and Technical College, Zhengzhou, Henan, China ' School of Electronic Engineering, Zhengzhou Railway Vocational and Technical College, Zhengzhou, Henan, China

Abstract: The maximum power tracking of the rooftop solar cells of intelligent buildings cannot be tracked quickly when the effective photovoltaic array is under uniform illumination because of the slow convergence speed. Therefore, a new method of maximum power tracking of the rooftop solar cells of intelligent buildings based on particle swarm optimisation algorithm is proposed. Firstly, the solar cell model is established, and the influence factors of temperature and light intensity are identified as the factors affecting the tracking effect. Then, the particle swarm optimisation algorithm is introduced to determine the initial position of the battery power parameters. Finally, based on the particle swarm optimisation algorithm, the maximum power tracking of solar cells on the roof of intelligent buildings is realised by solving the function repeatedly. The results show that the proposed algorithm has higher tracking accuracy and better dynamic response ability, and the tracking accuracy is improved by 3.7% and the maximum power point can be tracked again in a short time.

Keywords: mathematical model of photovoltaic cells; I-U characteristic equation; guided wave function; particle swarm optimisation; maximum power point tracking.

DOI: 10.1504/IJGEI.2025.145983

International Journal of Global Energy Issues, 2025 Vol.47 No.3, pp.283 - 301

Received: 30 May 2023
Accepted: 09 Nov 2023
Published online: 01 May 2025 *

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