基于QPSO的MPPT控制研究  

Research on MPPT Control Based on QPSO

作　　者：房俊龙[1] 张卫丹[1] 宋朝 宁长健 张悦[1] 高三策 张绍原 FANG Junlong;ZHANG Weidan;SONG Chao;NING Changjian;ZHANG Yue;GAO Sance;ZHANG Shaoyuan(College of Electrical and Information,Northeast Agricultural University,Harbin 150030,Heilongjiang,China)

机构地区：[1]东北农业大学电气与信息学院

出　　处：《电气传动》2019年第11期88-91,共4页Electric Drive

摘　　要：在复杂光照情况下,常规粒子群(PSO)算法实现的最大功率点跟踪(MPPT)用时较长,且易陷入局部极值,导致功率损失较为严重。针对此现象,引入量子粒子群(QPSO)算法对MPPT进行控制,并提出有效的收敛条件和重启条件;在无阴影、静态阴影和动态阴影3种光照情况下,分别对PSO和QPSO的MPPT控制进行仿真对比。分析表明:无论在何种光照情况下,基于QPSO的MPPT控制收敛速度更快,追踪效果更好,能够有效提高光伏阵列的光电转化效率。Under complex light conditions,the maximum power point tracking(MPPT)implemented by conventional particle swarm optimization(PSO)algorithm takes a long time,and it easily falls into local extremum,resulting in more serious power loss. For this phenomenon,the quantum-behaved particle swarm optimization(QPSO)algorithm was introduced to control the MPPT,and effective convergence conditions and restart conditions were proposed. In the absence of shadows,static shadows,and dynamic shadows,the MPPT control of the PSO and QPSO was simulated and compared. The analysis shows that the QPSO-based MPPT control converges more quickly and the tracking effect is better regardless of the lighting conditions,which can effectively improve the photovoltaic array′s photoelectric conversion efficiency.

关 键 词：粒子群算法 量子粒子群算法 最大功率点跟踪 

分 类 号：TM615[电气工程—电力系统及自动化]