基于全局学习自适应细菌觅食算法的光伏系统全局最大功率点跟踪方法

Photovoltaic System Global Maximum Power Point Tracking Method Based on the Global Learning Adaptive Bacteria Foraging Algorithm

为了使光伏发电系统输出功率最大化,最大功率点跟踪(MPPT)技术被广泛采用。当出现局部遮阴等外部天气条件变化时,会使得光伏功率特性曲线出现多峰现象,增加最大功率追踪过程的复杂性。传统的MPPT方法和软计算技术由于固定步长和随机性不足等缺点,可能无法跟踪到全局最大功率点(GMPP)。为此本文提出一种全局学习自适应细菌觅食算法,将全局学习机制和自适应步长策略引入到传统的细菌觅食算法中,以提高算法的求解精度和收敛速度。同时,采用直接控制法模型,并提出两步法MPPT控制策略,避免光伏系统输出功率趋于最大点时的功率振荡,提高系统的输出效率。仿真结果表明所提出的方法在动态环境条件下可以准确快速地跟踪GMPP。

In order to maximize the output power of photovoltaic power generation system,global maximum power point tracking(MPPT)technology is widely used.When the external weather conditions such as local shading change,the photovoltaic power characteristic curve will appear multi-peak phenomenon,which increases the complexity of the maximum power tracking process.Traditional MPPT methods and soft computing techniques may not be able to track the global maximum power point(GMPP)due to the fixed step size and randomness.Therefore,a global learning adaptive bacterial foraging algorithm was proposed in this paper.The global learning mechanism and adaptive step strategy were introduced into the traditional bacterial foraging algorithm to improve the accuracy and convergence speed of the algorithm.At the same time,the direct control model was adopted,and a two-step MPPT control strategy was proposed to avoid the power oscillation when the output power of the photovoltaic system tends to the maximum point,which could improve the output efficiency of the system.Simulation results show that the proposed method can track GMPP accurately and quickly under dynamic environment.