摘要
针对部分阴影条件下粒子群优化(PSO)算法追踪最大功率点时间较长与功率波动大的问题,提出一种基于万有引力与粒子群混合优化(GPSHO)算法的最大功率点追踪(MPPT)方法。该方法将万有引力搜索算法引入粒子群算法,在迭代过程中通过调节PSO算法的惯性权重、认知因子和社会因子提高算法的收敛速度,实现追踪全局最大功率点。仿真与实验结果表明:该方法能够在不同光照情况下精准地追踪全局最大功率点,其搜索速度大约比基于自适应惯性权重粒子群(APSO)算法的MPPT方法快1倍,功率振荡亦更小。
To reduce the long time required and the large power oscillation for the particle swarm optimization(PSO)algorithm to track the maximum power point under partially shaded conditions,an MPPT,or maximum power point tracking method,based on gravity and particle swarm hybrid optimization(GPSHO)is proposed.It introduces the gravity search algorithm into the PSO algorithm,and adjusts the inertia weight,cognitive factor and social factor of PSO algorithm to realize the global MPPT.Simulation and experimental results show that the GPSHO-based MPPT can accurately track the global maximum power point under different solar irradiance at a speed about twice that of the MPPT based on adaptive inertial weight PSO,with less power oscillation.
作者
黄荣赓
HUANG Ronggeng(School of Optoelectronic & Communication Engineering,Xiamen University of Technology,Xiamen 361024,China)
出处
《厦门理工学院学报》
2021年第3期29-36,共8页
Journal of Xiamen University of Technology
基金
福建省中青年教师教育科研项目(JAT190669)。
关键词
最大功率点追踪方法
粒子群优化算法
万有引力搜索算法
惯性权重
认知因子
社会因子
maximum power point tracking
particle swarm optimization algorithm
gravity search algorithm
inertia weight
cognitive factor
social factor
