Research On MPPT Optimization Technology Of Photovoltaic Power Generation System

Against the background of the global energy crisis and the increasingly serious ecological environment,renewable energy has received widespread attention around the world.As one of the clean energy sources,solar energy has become one of the most critical clean energy sources due to its inexhaustible advantages.Solar photovoltaic power generation technology is one of the important ways to effectively use solar energy,so it has great theoretical significance and market value for the research of photovoltaic power generation technology.This paper takes PV output maximum power tracking technology as the research object,and conducts in-depth research on the two most critical issues,namely,maximum power point tracking under uniform illumination and local shadow.First of all,to study the working principle of photovoltaic cells,build a mathematical model based on topological structure.The defects of traditional fuzzy controller input are analyzed,and a fuzzy control algorithm based on optimization function is proposed.The input of fuzzy controller is optimized to solve the problem of low efficiency of photovoltaic power generation caused by insufficient control accuracy of traditional fuzzy control.Secondly,in order to achieve global maximum power point tracking under partial shadows,this thesis selects the particle swarm optimization algorithm with better global optimization effect as the maximum power point tracking algorithm under multi-peak characteristics.Aiming at the problem of difficulty in choosing between local optimization and global optimization for particle swarm optimization,optimization of inertial weights,social learning factors and personal learning factors improves the global search speed in the early stage of the particle swarm optimization and the local convergence speed in the middle and late stages.Aiming at the phenomenon that the adaptability of some particles is too low,a particle elimination mechanism is added to further improve the convergence speed of particle swarm optimization.The results show that the particle swarm optimization algorithm based on factor optimization and elimination mechanism can improve the tracking speed of the maximum power point under local shadows and save power generation costs.Finally,MATLAB/SIMULINK tool is used to build a mathematical model under uniform illumination and local shadow,and the effectiveness of the algorithm is verified through simulation.