摘要
在部分阴影条件(PSC)下,光伏阵列呈现高度非线性的功率-电压特性。针对经典粒子群算法(PSO)易陷入局部最优、输出稳定后出现功率波动等问题,提出一种基于改进的自然选择粒子群算法(INSPSO)结合增量电导法(INC)的光伏最大功率点追踪(MPPT)控制策略。研究引入动态惯性权重、异步学习因子和自然选择机制,在分析寻优过程中对惯性权重和学习因子实时调整,并对群体进行自然选择操作以提高算法的全局寻优性能。仿真分析表明,所提算法在收敛速度和精度方面优势明显,且在追踪到最大功率点后的输出功率更平稳。
Under partial shading conditions(PSC),photovoltaic arrays exhibit highly nonlinear P-V characteristics.To address the issues of the classical particle swarm optimization(PSO)algorithm,such as the tendency to fall into local optima and power fluctuations after stabilization,the paper proposes a maximum power point tracking(MPPT)control strategy that combines an improved natural selection particle swarm optimization(INSPSO)combined with incremental conductance(INC).The proposed approach introduces dynamic inertia weigh,asynchronous learning factor,and a natural selection mechanism.During the optimization process,the inertia weight and learning factor are adjusted in real-time,while the natural selection is applied to the population to enhance the global optimization capability.Simulation results show that the strategy has clear advantages in convergence speed and accuracy.Moreover,the power output is more stable after reaching the maximum power point.
作者
陈刚
刘旭阳
李国雄
刘亚雄
CHEN Gang;LIU Xuyang;LI Gouxiong;LIU Yaxiong(School of Electrical and Information Engineering,Hunan University of Technology,Zhuzhou 412007,China;State Grid Zhuzhou Power Supply Company,Zhuzhou 412000,China)
出处
《智慧电力》
北大核心
2025年第2期58-64,共7页
Smart Power
基金
国家自然科学基金资助项目(62173136)。
关键词
光伏阵列
MPPT
动态部分遮阴
自然选择粒子群算法
photovoltaic array
MPPT
dynamic partial shading
natural selection particle swarm optimization algorithm
