基于改进自适应粒子群算法的MPPT追踪系统

MPPT Tracking System Design Based on an Improved Particle Swarm Optimization Algorithm

为优化最大功率点追踪(MPPT)技术的追踪精度和追踪时间,提出一种改进自适应粒子群算法(APSO)。对传统PSO算法进行优化,引入自适应惯性权重和非线性学习因子,使其在全局寻优-局部寻优-全局寻优状态下加速MPPT追踪,最后搭建光伏发电系统对自适应粒子群算法进行仿真验证。试验证明:相比于传统PSO算法,改进的APSO算法追踪精度更高,收敛速度更快。未遮挡环境(STC)恒温和变温下收敛速度提升了30.6%和39.2%,局部遮挡(PSC)恒温和变温下收敛速度提升了54.0%和53.7%,改进的APSO算法在PSC环境下更具优势;PSO算法最大功率稳定后占空比存在震荡现象,而APSO算法的占空比为稳定状态,提高了系统的稳定性能。

In view of an optimization of the tracking accuracy and tracking time of Maximum Power Point Tracking(MPPT)technology,an improved Adaptive Particle Swarm Optimization(APSO)algorithm has thus been proposed.An optimization can be achieved of the traditional PSO algorithm by introducing adaptive inertia weights and nonlinear learning factors so as to accelerate MPPT tracking in the global optimization-local optimization-global optimization state.Subsequently,a photovoltaic power generation system is to be established for an simulation and verification of the adaptive particle swarm optimization algorithm.Experimental results indicate that compared to traditional PSO algorithms,the improved APSO algorithm is characterized with a higher tracking accuracy and faster convergence speed.Under constant and variable temperatures in an unobstructed environment(STC),the convergence speed has increased by 30.6%and 39.2%respectively,while the convergence speed under constant and variable temperatures in a partial occlusion(PSC)has increased by 54.0%and 53.7%,showing a performance superiority of the improved APSO algorithm in PSC environments.Furthermore,the PSO algorithm exhibits oscillations in the duty cycle after stabilizing the maximum power,while the APSO algorithm maintains a stable duty cycle,thereby improving the overall stability of the system.