为了解决局部阴影下传统最大功率点追踪(maximum power point tracking,MPPT)算法容易陷入局部最优从而降低光伏系统发电效率的问题,本研究提出融合正弦余弦算法和自适应策略的布谷鸟优化算法(cuckoo search algorithm fusing sine cosine algorithm and adaptive strategy,AFCS),并应用于光伏全局MPPT控制中,以改善其收敛速度与追踪精度.设置多种光照情况,并与扰动观察法、花朵授粉算法和粒子群算法进行对比.经过Matlab/Simulink仿真验证,表明本算法拥有较快的收敛速度和较高的追踪精度,在各个光照条件下均能快速追踪到光伏阵列最大功率点,可以有效提高光伏系统的发电效率.
Abstract
In order to solve the problem that traditional maximum power point tracking(MPPT)algorithms,such as the perturbation observation method,tend to fall into the local optimum and thus reduce the power generation efficiency of the PV system under partial shading condition,this study proposes a cuckoo search algorithm fusing sine cosine algorithm and adaptive strategy(AFCS),and applies it to the global MPPT control of PV in order to improve its convergence speed and tracking accuracy.Multiple light conditions are set up and compared with perturbation observation method,flower pollination algorithm and particle swarm algorithm.After Matlab/Simulink simulation verifica-tion,the algorithm has faster convergence speed and higher tracking accuracy,and can quickly track to the maximum power point of the PV array under various lighting conditions,which can effectively improve the power generation efficiency of the PV system.
关键词
局部阴影/光伏阵列/融合算法/自适应策略/最大功率点追踪
Key words
partial shading condition/photovoltaic array/fusion algorithm/adaptive strategy/maxi-mum power point tracking
维普
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