A Compound MPPT Optimization Method Based on Natural Selection Particle Swarm-Incremental Conductance

A compound MPPT optimization method based on Natural Selection Particle Swarm and Incremental Conductance is proposed to solve the problem that the PV array P-U curve tends to exhibit multi-peak characteristics in actual working conditions, which leads to the local optimization of the traditional MPPT algorithm and the low conversion efficiency of the PV power generation system. In this method, a Natural Selection mechanism is introduced to replace the particles with low fitness in the iterative process of the particle swarm optimization algorithm. The Incremental Conductance algorithm is integrated for secondary power tracking to improve the relative maximum power point search rate, reduce the number of algorithm iterations and further achieve fast and accurate tracking of the global optimal solution. Simulation results show that the proposed optimization method has higher tracking accuracy and iteration speed compared with the PSO & INC algorithm, and can effectively improve the conversion efficiency of photovoltaic power generation systems and reduce power loss.