基于APSO算法的局部阴影下光伏最大功率跟踪研究  

Research on photovoltaic maximum power tracking under local shadow based on APSO algorithm

作　　者：殷梅 叶明月 宋景利 薛卫星 YIN Mei;YE Mingyue;SONG Jingli;XUE Weixing(Central Plains Xinhua Water Resources and Hydropower Investment Company,Zhengzhou 450016,China;Henan Zhijian Wisdom New Energy Co.,Ltd.,Zhengzhou 450018,China)

机构地区：[1]中原新华水利水电投资公司,河南郑州450016 [2]河南至坚智慧新能源有限公司,河南郑州450018

出　　处：《电气应用》2025年第3期41-46,共6页Electrotechnical Application

摘　　要：针对传统的最大功率点追踪(Maximum Power Point Tracking,MPPT)算法在局部阴影条件下易陷入局部最优以及粒子群优化算法(Particle Swarm Optimization,PSO)存在的收敛速度慢和易陷入局部最优等问题,提出一种基于自适应粒子群优化(Adaptive Particle Swarm Optimization,APSO)算法的复合MPPT方法。在APSO中,初始化阶段使用拉丁超立方抽样代替随机初始化,使用自适应惯性权重策略来平衡算法的探索和开发,在提高算法收敛速度的同时,避免陷入局部最优解。通过仿真实验证明该算法在局部阴影下能够跳出局部最优,快速收敛到最大功率点处,与LPSO、PSO算法进行对比,所提算法具有更快的追踪速度、更高的追踪效率和更强的鲁棒性。The traditional Maximum Power Point Tracking(MPPT)algorithm is easy to fall into the local local optimum under the local shadow condition,and the Particle Swarm Optimization Algorithm(PSO).In this paper,a composite MPPT tracking method based on Adaptive particle swarm optimization(APSO)is proposed.In APSO,Latin hypercube sampling is used to replace random initialization,and adaptive inertial weight strategy is used to balance the exploration and development of the algorithm,so as to improve the convergence speed of the algorithm and avoid falling into the local optimal solution.Through simulation experiments,the proposed algorithm can jump out of the local optimal under the local shadow and quickly converge to the maximum power point.Compared with LPSO and PSO algorithms,the proposed algorithm has faster tracking speed,higher tracking efficiency and stronger robustness.

关 键 词：最大功率点追踪 局部阴影 拉丁超立方抽样 自适应惯性权重 APSO 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]