局部遮荫下融合PSO&INC的MPPT研究OA

Changes in the maximum power of photovoltaic cells under partial shading directly affect the efficiency of photovoltaic systems.To address this issue,a hybrid method combining particle swarm optimization(PSO)and the incremental conductance(INC)method is proposed.By performing mean partitioning of the initial population,the corresponding multi-peak power curve can be effectively divided into different regions,thereby significantly improving the search speed.Then,the particle positions are updated according to a strategy combining the number of iterations and the global maximum of the population.Based on the calculated power variation,small-step incremental conductance(INC)is further adopted for local optimization,which effectively reduces the number of iterations and saves iteration time.Simulations under different scenarios of partial shading and dynamic shading were carried out in Simulink.The results show that,compared with the original particle swarm optimization algorithm,the proposed hybrid algorithm can achieve a photovoltaic efficiency of 98.20%,with the optimization speed improved by 59.46%.Experimental results further verify that the proposed power tracking method can effectively track the maximum power point of the photovoltaic array under partial shading,with the tracking speed increased by 50%and the tracking accuracy reaching 98.91%.