Research On MPPT Control Strategy Of Photovoltaic Power Generation System Under Different Lighting Conditions

In the background of the global energy revolution and China’s "carbon peak" and "carbon neutral" goals,solar energy is widely developed and utilized because of its green,safe and widely distributed advantages.Photovoltaic(PV)system photoelectric conversion efficiency is affected by the ambient temperature,light intensity,local shade and other factors,in real life,due to cloud cover,building shading and other factors,resulting in uneven light to the photovoltaic cell,the output P-V characteristic curve becomes multi-peak curve,easy to make the traditional maximum power point tracking(Maximum Power Point Tracking,MPPT)algorithm.In order to reduce the power loss and improve the energy utilization,the following research is done in this paper.Firstly,the principle and output characteristics of PV cell are analyzed,and the output characteristics of P-V and I-V curves of PV arrays in three shading modes,namely uniform shading,lateral shading and vertical shading,are investigated on Simulink platform,and the mechanism of multi-peak phenomenon of PV arrays in lateral shading mode is also analyzed.Secondly,to address the problems of slow tracking speed and low tracking accuracy of conventional MPPT control methods under local shading,Lion Swarm Optimization(LSO)is introduced in this paper,and the principle of LSO is analyzed.The improved Lion Swarm Optimization(ILSO)algorithm with enhanced local search capability is proposed to address the problem of insufficient local search capability at the later stage of the Lion Swarm algorithm.ILSO improves the local search capability of LSO,but also inherits the advantage of strong global search capability of LSO,so that ILSO can track to the maximum power point more quickly and accurately.In order to reflect the superiority of ILSO performance,ten typical multi-peak test functions are selected for simulation testing.Finally,a model of the PV system based on the improved Lion Group optimization algorithm is built in Matlab/Simulink platform and simulated under three conditions of uniform illumination,partial shading and dynamic shading respectively.