MPPT Control Strategy of PV Based on Improved Shuffled Frog Leaping Algorithm under Complex Environments

This work presents a maximum power point tracking (mppt) based on the particle swarm optimization (pso) improved shuffled frog leaping algorithm (psfla). the swarm intelligence algorithm (sia) has vast computing ability. the mppt control strategies of pv array based on sia are attracting considerable interests. firstly,the psfla was proposed by adding the inertia weight factor w of pso in standard sfla to overcome the defect of falling into the partial optimal solutions and slow convergence speed. the proposed psfla algorithm increased calculation speed and excellent global search capability of mppt. then,the psfla was applied to mppt to solve the multiple extreme point problems of nonlinear optimization. secondly,for the problems of mppt under complex environments,a new mppt strategy of the psfla combined with recursive least square filtering was proposed to overcome the measurement noise effects on mppt accuracy. finally,the simulation comparisons between psfla and sfla algorithm were developed. the experiment and comparison between pslfa and pso algorithm under complex environment were executed. the simulation and experiment results indicate that the proposed mppt control strategy based on psfla can suppress the measurement noise effects effectively and improve the pv array efficiency. © 2017 xiaohua nie and haoyao nie.