基于改进PSO算法在局部遮阴下光伏系统MPPT中的应用  

Application of Photovoltaic System MPPT under Local Shading Based on Improved PSO Algorithm

作　　者：朱霄 周振雄[1] 李镇 潘若妍 ZHU Xiao;ZHOU Zhenxiong;LI Zhen;PAN Ruoyan(College of Electrical and Information,Beihua University,Jilin 132021,China)

机构地区：[1]北华大学电气与信息工程学院,吉林吉林132021

出　　处：《北华大学学报（自然科学版）》2023年第3期394-399,共6页Journal of Beihua University（Natural Science）

基　　金：国家自然科学基金项目(61179012);吉林省发改委项目(2019C058-1);吉林省教育厅科学技术研究项目(JJKH20200042KJ,JJKH20180342KJ);吉林省科技发展计划项目(YDZJ202303CGZH001).

摘　　要：在局部遮阴条件下,光伏阵列的功率输出曲线存在多个峰,为确保光伏系统能够更好地工作在最大功率点,提出一种改进粒子群(PSO)算法.自适应调整惯性权重和学习因子,并引入差分进化(DE)算法中的变异、交叉等操作来丰富粒子多样性,使算法不仅有更快的收敛速度,而且在遮阴条件下也能精准追踪到最大功率点.在Simulink中搭建系统仿真模型进行仿真试验.结果表明,改进粒子群算法能够明显提高追踪最大功率点的速度和精度.Under the condition of local shading,the power output curve of photovoltaic array has multiple peaks.In order to ensure that the photovoltaic system can work better at the maximum power point,an improved particle swarm optimization(PSO)algorithm is proposed.The algorithm adaptively adjusted the inertia weight and learning factor,and introduced the mutation,crossover and other operations of differential evolution(DE)algorithm to enrich the diversity of particles,so that the whole algorithm not only has faster convergence speed,but also can accurately track the maximum power point under shading condition.The system simulation model is built in Simulink,and the simulation results show that the improved particle swarm optimization algorithm can significantly improve the speed and accuracy of MPPT tracking.

关 键 词：局部遮阴 最大功率跟踪 粒子群 差分进化 

分 类 号：TM615[电气工程—电力系统及自动化]