基于IBFA的局部阴影下光伏最大功率点追踪研究  

Research on Photovoltaic Maximum Power Point Tracking Under Local Shadow Based on IBFA

作　　者：李强 刘文华 雷奇 LI Qiang;LIU Wen-hua;LEI Qi(Shanxi Luneng Jingbian Wind Power Generation Co.Ltd.,Yulin 718505,China;Shanxi Huanghe Energy Co.Ltd.,Xi′an 710061,China)

机构地区：[1]陕西鲁能靖边风力发电有限责任公司,陕西榆林718505 [2]陕西黄河能源有限责任公司,陕西西安710061

出　　处：《电气开关》2023年第3期82-86,共5页Electric Switchgear

摘　　要：最大功率点追踪(Maximum Power Point Tracking,MPPT)技术是提高光伏发电效率的重要手段之一。针对传统MPPT不能满足局部阴影光照下寻优要求,本文提出一种改进细菌觅食法(Improved Bacteria Foraging Optimization Algorithm,IBFA)。通过引入全局学习方向,牵引细菌种群趋化方向,以此增加了算法的收敛速度;增加自适应步长机制,改善步长多样性,使得算法在寻优过程中可以跳出局部最优,收敛后期更加精确。通过仿真实验测试,该算法在局部阴影下能够跳出局部最优,快速收敛到最大功率点处,同时与粒子群算法(Particle Swarm optimization,PSO)相比性能更加优越。Maximum Power Point Tracking technology is one of the important means to improve the efficiency of photovoltaic power generation.In view of the fact that traditional MPPT cannot meet the optimization requirements under partial shadow illumination,this paper proposes an improved bacterial foraging method.By introducing the global learning direction,the chemotaxis direction of the bacterial population is pulled,thereby increasing the convergence speed of the algorithm;adding an adaptive step size mechanism to improve the step size diversity,so that the algorithm can jump out of the local optimum in the process of optimization,and the convergence period is late.more precise.Through the simulation experiment test,the algorithm can jump out of the local optimum under the local shadow,quickly converge to the maximum power point,and has better performance than the particle swarm algorithm.

关 键 词：太阳能发电 最大功率点追踪 IBFA算法 

分 类 号：TM91[电气工程—电力电子与电力传动]