基于改进长鼻浣熊优化算法的光伏MPPT研究  

Maximum Power Point Tracking Strategy for Photovoltaic Power Projects based on Improved Coati Optimization Algorithm

作　　者：许海雷 周晓韡 叶罗 XU Hailei;ZHOU Xiaowei;YE Luo(CHN Energy Taizhou Power Generation Co.,Ltd.,Taizhou 225321,China)

机构地区：[1]国家能源集团泰州发电有限公司,江苏泰州225321

出　　处：《水电与新能源》2025年第2期33-37,共5页Hydropower and New Energy

摘　　要：在光照不均匀或局部阴影下,光伏阵列的输出呈现多极值现象,传统的最大功率点跟踪(maximum power point tracking,MPPT)难以搜索全局最优输出功率。为了提升局部阴影下光伏阵列的发电效率,提出一种基于改进长鼻浣熊优化算法(ICOA)的PV-MPPT策略,结合立方混沌映射,以提高算法收敛速度和全局搜索能力。MATLAB/Simulink仿真结果表明,相较于传统智能算法,采用ICOA-MPPT控制策略,提高了跟踪速度和精度。Under uneven lighting or partial shading conditions,the output of photovoltaic(PV)arrays exhibits a multi-extremum phenomenon.It is difficult for traditional maximum power point tracking(MPPT)strategy to search for the global optimal output power.To improve the power generation efficiency of photovoltaic arrays under partial shading condition,a PV-MPPT strategy based on the improved coati optimization algorithm(ICOA)is proposed,which improves the convergence speed and global search ability by combining the cubic chaotic mapping.Simulation results in MATLAB/Simulink show that the proposed ICOA-MPPT control strategy improves the tracking speed and accuracy compared to traditional intelligent algorithms.

关 键 词：光伏 局部阴影 最大功率追踪 长鼻浣熊优化算法 

分 类 号：TM651[电气工程—电力系统及自动化]