基于改进人工鱼群算法的光伏系统MPPT研究

发布时间：2019-05-24 12:12

【摘要】：针对光伏系统最大功率点跟踪(MPPT)传统算法的不足,提出一种改进的人工鱼群算法(IAFSA),该算法将扰动观察法(PO)引入到人工鱼群算法。首先利用扰动观察法实时性强和跟踪快速的特点找到系统的最大功率点,然后由人工鱼群算法对全局最大功率点进行快速搜索跟踪,确定功率点极值,避免了扰动观察法使功率最大点陷入局部极值的问题。应用Matlab仿真,分别以标准环境温度下光照均匀和光照部分被遮蔽以及不同环境温度下光照部分被遮蔽3种条件对IAFSA与传统的PO和PSO算法最大功率点跟踪效果进行比较,仿真结果表明,采用IAFSA算法可有效跟踪光伏系统的最大功率点,提高系统的应用效率。
[Abstract]:In view of the shortcomings of the traditional (MPPT) algorithm for maximum power point tracking in photovoltaic systems, an improved artificial fish swarm algorithm (IAFSA), is proposed, which introduces the disturbance observation method (PO) into the artificial fish swarm algorithm. Firstly, the maximum power point of the system is found by using the characteristics of strong real-time performance and fast tracking of the disturbance observation method, and then the global maximum power point is quickly searched and traced by the artificial fish swarm algorithm to determine the extreme value of the power point. The problem of maximum power point falling into local extreme value by disturbance observation method is avoided. Using Matlab simulation, the maximum power point tracking effect of IAFSA is compared with that of traditional PO and PSO algorithms under three conditions: uniform lighting and shading of lighting part at standard ambient temperature and masking of lighting part at different ambient temperatures, respectively. the maximum power point tracking effect of PO and PSO algorithm is compared with that of traditional PO and PSO algorithm. The simulation results show that the IAFSA algorithm can effectively track the maximum power point of photovoltaic system and improve the application efficiency of the system.
【作者单位】：江苏科技大学电子信息学院;
【基金】：基金项目: 江苏高校优势学科建设工程(PAPD)
【分类号】：TP18;TM615

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