复杂遮蔽条件下光伏多峰出力特征及GMPPT控制  

Photovoltaic multi-peak output characteristics and GMPPT control under complex shaded condition

作　　者：陈明轩[1] 武建文[1] 马速良[1] 黄炼[1] 

机构地区：[1]北京航空航天大学自动化科学与电气工程学院,北京100083

出　　处：《北京航空航天大学学报》2017年第6期1141-1148,共8页Journal of Beijing University of Aeronautics and Astronautics

基　　金：国家自然科学基金(51377007);高等学校博士学科点专项科研基金(20131102130006)~~

摘　　要：针对光伏发电系统中最大功率点跟踪(MPPT)算法在遮蔽情况下失效问题,提出了一种基于δ势阱的量子粒子群全局MPPT(GMPPT)算法。结合光照强度变化时的光伏多峰值出力特征,从光伏最大功率点变迁角度出发,分析常规MPPT算法存在搜索盲区的原因,说明GMPPT寻优必要性。提出一种提高粒子多样性、搜索速度及收敛精度的量子行为粒子群优化(QPSO)算法。在MATLAB/SIMSCAPE平台下,结合算例分析,对比标准粒子群优化(PSO)算法,验证所提优化算法在有效GMPPT的情况下,具有参数少、搜索快的特点,同时全局搜索能力强,防早熟效果明显,适用于GMPPT的实现。Aimed at solving the failure problem of the maximum power point tracking （MPPT） algorithm caused by partially shaded condition in the photovohaic power generation system, a global maximum power point tracking （GMPPT） algorithm based on 8-potential well is proposed. Based on the photovoltaic multi- peak output characteristics when the illumination intensity is changing, the reason of searching blind spot in conventional MPPT algorithm is analyzed in terms of maximum power point transition, and the necessity of GMPPT optimization is explained. A quantum-behaved particle swarm optimization （QPSO） algorithm is pro- posed to improve the particle diversity and increase the search speed and convergence accuracy. The algorithm was verified by MATLAB/SIMSCAPE and compared with the standard particle swarm optimization （PSO） algorithm. The results show that the proposed algorithm can track the global maximum power point effectively with fast searching speed, reducing the dependency on parameters and avoiding premature convergence of the algorithm.

关 键 词：光伏发电 光伏阵列 局部阴影 全局最大功率点跟踪(GMPPT) 量子行为粒子群优化(QPSO)算法 

分 类 号：TM615[电气工程—电力系统及自动化]