摘要
光伏阵列在局部遮荫条件下,会产生多峰值的功率-电压(P-U)特性,为了迅速精准地实现最大功率点追踪以避免大量的能源损失,提出一种改进的天鹰优化算法,通过Circle混沌映射及反向学习策略合理分配初始种群位置,以缩短算法寻优时间,同时对天鹰优化算法中的短滑翔攻击进行螺旋形优化,并结合鲸鱼优化算法改善天鹰优化算法局部最优停滞以及提高收敛速度。多种智能算法对比仿真和实验结果表明,相较于粒子群算法及鲸鱼优化算法,改进天鹰优化算法在静态、动态局部遮荫情况下均能更快、更平稳精准地搜索到全局最大功率点。
The photovoltaic array will produce multi-peak P-U characteristics under partial shading conditions.Aiming at the problem of how to quickly and accurately realize maximum power point tracking(MPPT)to avoid a large amount of energy loss,this paper proposes an improved aquila optimization(AO)algorithm,which uses Circle chaotic mapping and reverse learning strategy to reasonably allocate the initial population position,so as to shorten the optimization time of the algorithm.At the same time,spiral optimization is carried out for the short gliding attack in aquila optimization algorithm.The whale optimization algorithm is combined to improve local optimal stagnation and convergence speed.Simulations and experiments demonstrate that,in comparison to particle swarm optimization(PSO),whale optimization algorithm(WAO)and aquila optimization algorithm,the algorithm can search the global maximum power point with greater speed,accuracy and suppleness under both static and dynamic partial shading conditions.
作者
姚天棋
柴琳
肖凡
刘惠康
徐万万
YAO Tianqi;CHAI Lin;XIAO Fan;LIU Huikang;XU Wanwan(The College of Information Science and Engineering,Wuhan University of Science and Technology,Wuhan 430081,China)
出处
《热力发电》
CAS
CSCD
北大核心
2023年第12期98-105,共8页
Thermal Power Generation
基金
国家自然科学基金面上项目(51877161)。
