Abstract：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.
商立群, 朱伟伟. 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[J]. 电工技术学报, 2019, 34(12): 2606-2614.
Shang Liqun, Zhu Weiwei. Photovoltaic System Global Maximum Power Point Tracking Method Based on the Global Learning Adaptive Bacteria Foraging Algorithm. Transactions of China Electrotechnical Society, 2019, 34(12): 2606-2614.
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2019, Vol. 34 
