Maximum Power Point Tracking for Photovoltaic Systems Based on Adaptive Bald Eagle Search Algorithm

Under partial shading conditions, the P-V characteristic curve of the photovoltaic system shows multi-peak characteristics, which makes the traditional maximum power point tracking algorithm easy to fall into the local maximum power point, thus reducing its tracking efficiency. Therefore, this paper proposes an adaptive bald eagle search algorithm. Based on the traditional bald eagle search algorithm, the algorithm introduces Gaussian mixture adaptive walking strategy, progressive diving adaptive switching strategy and bald eagle swarm size adjustment mechanism to enhance the global search and local optimization ability of the algorithm and improve its convergence accuracy and speed. Under the Simulink simulation platform, the algorithm is applied to the maximum power tracking of a photovoltaic power generation system, and compared with the traditional bald eagle search algorithm, particle swarm optimization algorithm and grey wolf optimization algorithm. The results show that the proposed algorithm has faster tracking speed and higher accuracy than the existing three algorithms in four typical scenarios, and the power fluctuation in the tracking process is smaller, which can improve the power generation of photovoltaic system.