基于改进鹰栖息优化算法的光伏系统MPPT控制  

MPPT CONTROL OF PHOTOVOLTAIC SYSTEM BASED ON IMPROVED EAGLE PERCHING OPTIMIZATION

作　　者：李炜康 刘婷婷 王哲铭 于文英 卢武 刘永生 Li Weikang;Liu Tingting;Wang Zheming;Yu Wenying;Lu Wu;Liu Yongsheng(Institute of Solar Energy,Shanghai University of Electric Power,Shanghai 201306,China)

机构地区：[1]上海电力大学太阳能研究所,上海201306

出　　处：《太阳能学报》2025年第1期662-668,共7页Acta Energiae Solaris Sinica

基　　金：国家自然科学基金(51971128,52171185);上海市优秀学术/技术带头人计划(20XD1401800)。

摘　　要：提出一种基于改进鹰栖息优化算法的最大功率追踪控制,算法首先对整个区域进行随机抽样,通过目标函数在抽样点寻找最优解,再对最优解进行二次抽样,从而实现从全局搜索到局部搜索的过渡。并在此基础上,引入适应值变量作为算法的反馈参数,实现了从全局搜索到局部搜索的自适应转变。仿真结果表明,与传统鹰栖息优化算法、布谷鸟算法及粒子群算法相比,所提算法在均匀光照,静态、动态局部阴影情况下均具有追踪速度快、收敛精度高和前期振荡小的特点,能有效提升光伏系统的最大功率跟踪效率和精度。In this paper,a maximum power tracking control based on the improved eagle perching optimization algorithm is proposed.The algorithm first randomly samples the whole area,finds the optimal solution at the sampling point through the objective function,and then samples the optimal solution twice,so as to realize the transition from global search to local search.On this basis,the adaptive variable is introduced as the feedback parameter of the algorithm,and the adaptive transformation from global search to local search is realized.The simulation results show that compared with the traditional eagle rooster optimization algorithm,Cuckoo algorithm and particle swarm optimization algorithm,the proposed algorithm has the characteristics of fast-tracking speed,high convergence accuracy and small early oscillation under uniform illumination,static and dynamic local shadow conditions,and can effectively improve the maximum power tracking efficiency and accuracy of photovoltaic systems.

关 键 词：光伏系统 最大功率追踪 光照条件 改进鹰栖息优化算法 局部阴影情况 

分 类 号：TM615[电气工程—电力系统及自动化]