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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(P&O)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P&O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。
In order to overcome the shortcomings of the traditional MPPT algorithm, an improved artificial fish school algorithm (IAFSA) is proposed, which introduces the disturbance observer (P & O) algorithm into the artificial fish swarm algorithm. Firstly, the maximum power point of the system is found by using real-time perturbation observation method and fast tracing. Then, the artificial fish swarm algorithm is used to search and track the global maximum power point rapidly to determine the extreme value of the power point and avoid the disturbance observation method to maximize the power Point into the local extreme problem. Matlab simulation was used to compare the maximum power point tracking effect between IAFSA and traditional P & O and PSO algorithms respectively under uniform ambient light illumination and light exposure and light exposure at different ambient temperatures. The simulation results show that, The IAFSA algorithm can effectively track the maximum power point of the PV system and improve the system’s application efficiency.