基于PSO-GWO算法的局部阴影光伏MPPT研究  

Research on Partial Shading of Photovoltaic MPPT Based on PSO-GWO Algorithm

作　　者：许爱华[1] 王智煜 贾皓天 袁文俊 XU Aihua;WANG Zhiyu;JIA Haotian;YUAN Wenjun(School of Electrical and Information Engineering,Northeast Petroleum University,Daqing 163318,China)

机构地区：[1]东北石油大学电气信息工程学院,黑龙江大庆163318

出　　处：《吉林大学学报（信息科学版）》2024年第5期781-789,共9页Journal of Jilin University（Information Science Edition）

摘　　要：针对在局部阴影条件下,光伏阵列的功率-电压特性曲线呈现多个峰值,传统群体智能优化存在收敛速度慢、振荡幅度大和易陷入局部最优等问题,提出一种基于PSO-GWO(Particle Swarm Optimization-Grey Wolf Optimization)算法的MPPT(Maximum Power Point Tracking)控制方法。该算法引入余弦规律变化的收敛因子,平衡GWO算法的全局搜索与局部搜索能力;引入PSO算法,提高灰狼个体与自身经验之间的信息交流。仿真结果表明,提出的PSO-GWO算法在局部阴影条件下不仅能快速收敛,而且功率输出震荡幅度更小,有效提升了局部遮阴条件下光伏阵列的最大功率跟踪效率和精度。Under local shading conditions,the power-voltage characteristic curves of photovoltaic arrays show multiple peaks,and traditional population intelligence optimization suffers from slow convergence,large oscillation amplitude and the tendency to fall into local optimality.To address the above problems,an MPPT(Maximum Power Point Tracking)control method based on the PSO-GWO(Particle Swarm Optimization-Grey Wolf Optimization)algorithm is proposed.The algorithm introduces a convergence factor that varies with the cosine law to balance the global search and local search ability of the GWO algorithm;the PSO algorithm is introduced to improve the information exchange between individual grey wolves and their own experience.Simulation results show that the proposed PSO-GWO algorithm not only converges quickly under local shading conditions,but also has a smaller power output oscillation amplitude,effectively improving the maximum power tracking efficiency and accuracy of the PV(Photovoltaic)array under local shading conditions.

关 键 词：最大功率点追踪 灰狼算法 粒子群算法 局部阴影 

分 类 号：TN710[电子电信—电路与系统] TM914.4[电气工程—电力电子与电力传动]