Research On Photovoltaic Maximum Power Point Tracking Based On Improved PSO Algorithm

At present,China’s energy resources are decreasing day by day,and the global environment is getting worse.This phenomenon makes our country focus on the development and utilization of clean energy.Today’s photovoltaic cells will cause great loss when used,and the only solution is to improve the utilization rate.At present,it is recognized that the best method is the Maximum Power Point Tracking(MPPT)algorithm,but the current MPPT algorithm is not particularly mature,and there are still some problems such as slow tracking speed and low accuracy.Therefore,this thesis mainly studies the photovoltaic MPPT algorithm under single peak and multi-peak conditions.Firstly,this thesis briefly introduced the background and significance of the research of solar photovoltaic power generation.And set up the mathematical model of photovoltaic cell.In the experiment,the output characteristics of photovoltaic cells were simulated under the condition that the illumination variable was uniform or local shading.Then,the principle and process of common single peak MPPT algorithm were introduced.And built a model to control the light intensity as the variation for simulation.Analyzed the influence of external factors on their characteristics when they changed.Then,aiming at the problems that the traditional single peak algorithm can’t stabilize at the maximum power point,and the search time is long.On this basis,an improved step prediction method is proposed.Firstly,the maximum power point voltage is predicted by mathematical formula,and then the step size is controlled by the pressure difference angle under different conditions.Compared with the traditional single peak algorithm,the simulation results show that the tracking speed and accuracy of the proposed method are improved,which verifies the feasibility of the prediction method.At last,when there is partial shading,the characteristic curve of photovoltaic array will have multiple peaks,and so on.(Particle Swarm Optimization,PSO)and its parameters are studied emphatically.An improved particle swarm optimization(IPSO)algorithm is proposed.According to the global search ability of the algorithm in the early stage,the local search ability of the algorithm is increased in the later stage.There are two learning factors to improve inertia weight.The improved algorithm is compared with the standard particle swarm optimization(PSO)algorithm,disturbance observation method,etc.The results show that IPSO algorithm can jump out of the local optimum in the case of local shadow,and quickly and accurately track to the maximum power point.