基于免疫细菌觅食算法的大容量光伏阵列GMPPT算法

Large-capacity Photovoltaic Array's GMPPT Technology Based on the Immune Bacterial Foraging Optimization Algorithm

老化、温度变化和局部阴影等引起的电池电气特性不同,使光伏阵列P-U曲线出现多个功率峰值点。大容量光伏阵列组件数多,其多峰值问题比小容量光伏阵列更常见和复杂。该文首先根据局部阴影条件下光伏阵列分段函数型输出特性,建立其S函数模型。然后提出免疫细菌觅食算法,实现大容量光伏阵列全局最大功率点跟踪(global maximum power point tracking,GMPPT),利用细菌觅食算法的随机选取方向特性和免疫选择算子,实现时变环境下全局最大功率点的动态跟踪,将所有跟踪到的全局最大功率点保存到全局最大功率点记忆池,再利用全局最大功率点记忆池初始化群体和产生迁移个体新位置,加快重复出现全局最大功率点的跟踪速度。仿真结果表明,免疫细菌觅食算法在动态和重复出现局部阴影条件下都有良好的GMPPT跟踪定位能力。

Photovoltaic array＇s power-voltage curve appearing multiple local maximum power points is caused by solar cells＇ non uniform electrical characteristics arising from degradation, temperature variations, partial shading, et al. Because of using more photovoltaic modules, the large-capacity photovoltaic arrays are more vulnerable to the multiple local maximum power points problem. First, this paper built the S-Function model of the large-capacity photovoltaic array under partially shaded conditions according to its corresponding output characteristics. Then, the immune bacterial foraging optimization algorithm（IBFOA） was proposed by combining bacterial foraging optimization algorithm（BFOA） and artificial immune algorithm（AIA）. By introducing BFOA＇s means of choosing directions of swimming randomly and AIA＇s immune selection operator, the IBFOA tracked the global maximum power point（GMPP） in varying environments successfully. Meanwhile, the IBFOA saved those tracked GMPPs in the GMPP database. On the basis of the established GMPP database, the IBFOA initialized the initial population and generated new dispersed bacteria to track reappearing GMPPs faster. The simulation results prove that the IBFOA performs well in tracking the GMPP of the large-capacity photovoltaic array under dynamic and reappearing partially shaded conditions.