Photovoltaic MPPT control under local shade based on improved TSO algorithm
The P-U characteristic curve of a photovoltaic array exhibits multi-peak characteristics in partially shaded environments,leading to the inefficiency of conventional maximum power point tracking(MPPT)algorithm in tracking the maximum power.To address this issue,this paper proposes a two-layer control model for photovoltaic MPPT based on an improved tuned swarm optimization(TSO)algorithm.In the upper layer,the Levy flight strategy and polynomial mutation strategy are embedded into tuna algorithm,creating the Levy-polynomial mutation tuna swam optimization(LPTSO)to search for the global maximum power point.In the lower layer,the perturbation observation method is employed to locally track the global maximum power point,thereby reducing power oscillations in local shading environments.The two-layer control model is applied to the photovoltaic MPPT simulation system,and the simulation experimental results show that,for multi-peak MPPT control,the proposed model achieves significant improvements in convergence speed,tracking efficiency,power oscillations,etc.In conclusion,the proposed two-layer control model for photovoltaic MPPT effectively addresses the issue of maximum power tracking failure in partially shaded environments.
photovoltaic arraylocal shadingmaximum power point trackingtuna algorithmperturbation observation method
万方数据
维普
