摘要
局部遮蔽条件(PSC)下,传统最大功率点跟踪(MPPT)算法会陷入局部极值,智能算法追踪时间过长。针对上述问题,提出了一种基于自适应种群粒子群算法(APPSO)的MPPT控制方法,引入全局和局部粒子密度的概念,并设计了两种自适应调整的粒子种群数量的机制。对该方法与常规粒子群算法(PSO)在均匀光照和PSC下分别进行了对比。仿真和实验结果均表明,在PSC下APPSO可迅速、准确地追踪到全局最大功率点(GMPP),追踪时间仅为PSO的50%左右。
Under partial shading conditions （ PSC ）, conventional maximum power point tracking （MPPT） methods would get trapped in local extreme and intelligent algorithms are time-consuming.To solve these problems, an adaptive population particle swarm optimization（APPSO） is proposed.The algorithm introduces the concepts of the global and local firefly density during each iteration, and devises two elimination mechanisms to adaptively adjust the population number.The proposed method is compared with original particle swarm optimization（PSO） under normal irradiance condition and PSC.Simulation and experimental results demonstrate that the APPSO can immediately and accurately track the global maximum power point （GMPP） under PSC, and the tracking time is only about haft of PSO.
作者
石季英
凌乐陶
薛飞
李雅静
SHI Ji-ying LING Le-tao XUE Fei LI Ya-jing(Tianjin University, Tianjin 300072, China)
出处
《电力电子技术》
CSCD
北大核心
2017年第5期27-30,共4页
Power Electronics
基金
国际科技合作专项项目资助(2013DFA11040)
国家自然科学基金资助项目(61571324)
关键词
最大功率点追踪
粒子群算法
遮蔽情况
maximum power point tracking
particle swarm optimization
partial shading condition
