Photovoltaic MPPT control based on hybrid improved adaptive particle swarm optimization algorithm
The conventional particle swarm algorithm for maximum power tracking of multi-peak photovoltaic arrays under partial shading conditions is prone to falling into local optimum and other problems,which seriously affect the normal operation and output efficiency of the system.To address these issues,this paper improves the conventional particle swarm algorithm and conducts relevant simulations and verifications.First,the topology of the photovoltaic power generation system and the control method are theoretically analysed,and the relevant parameters of the boost circuit are deduced through the establishment of the photovoltaic array model.Meanwhile,the iterative formula for updating the particles and the algorithmic process of the traditional particle swarm algorithm are analysed,and the chaotic mapping function is introduced to initialize the particle population,which can solve the problem of the uneven distribution of the initialized population efficiency.The nonlinear dynamic inertia weights of the particle swarm are also introduced.Then,the nonlinear dynamic inertia weight coefficients and dynamic learning factors are introduced to design the adaptive particle swarm optimisation algorithm,and the maximum power point tracking algorithm(IAPSO)based on hybrid improved adaptive particle swarm is built,which addresses the problems of the particle swarm algorithm easily falling into the local optimum and the wide fluctuation of the steady state under the partially shaded conditions.Finally,our control method is validated by Matlab/Simulink simulation software,and the performance of the improved algorithm is tested and compared with the traditional method under different lighting conditions.Our results show the IAPSO algorithm more easily adapts to changes in lighting conditions than the conventional maximum power tracking algorithm and achieves fast and high-precision maximum power point tracking of PV arrays,which effectively tackles the local optimum,steady state oscillation,and slow response speed in conventional maximum power point tracking method.
photovoltaic systempartial shading conditionmaximum power point trackingadaptive particle swarm optimizationhybrid improvement
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