扩展记忆优化人工鱼群算法在光伏系统MPPT的仿真研究

Simulation Research on MPPT of Photovoltaic System Based on EP-FSA

针对不均匀光照下,光伏发电系统中的光伏阵列输出功率曲线呈现多峰特性,传统光伏阵列最大功率点跟踪(MPPT)监控算法易陷入局部最优解误区,提出一种扩展记忆优化人工鱼群算法(EP-FSA)。该算法在标准鱼群算法上引入速度参数和记忆行为,既保证鱼群算法的全局搜索能力,又增加了记忆算法的快速收敛性,快速得出全局最优解。仿真分析证明,扩展记忆优化人工鱼群算法较持续扰动法和标准人工鱼群算法具有更快的收敛速度和更强的全局搜索性能。

Under uneven illumination,the output power curve of the photovoltaic array in the photovoltaic power generation system shows a multi-peak characteristic.The traditional maximum power point tracking point(MPPT)monitoring algorithm of photovoltaic arrays is easy to fall into the misunderstanding of local optimization.Therefore,an extended memory optimization artificial fish swarm algorithm(EP-FSA)is proposed.This algorithm introduces speed parameters and memory behavior to the standard fish school algorithm,which not only guarantees the global search ability of AFSA algorithm,but also increases the fast convergence of the memory algorithm,and quickly obtains the global optimal solution.Simulation analysis proves that the EP-FSA has faster convergence speed and stronger global search performance than the continuous perturbation method and the standard AFSA.