摘要
针对传统最大功率点跟踪算法在寻优过程中难以跳出局部最优值的问题,笔者提出一种改进细菌觅食算法。该算法通过自适应调整游动步长和迁移概率,提高算法的追踪速度和精度,引入全局学习方向加强细菌群体间交流,采用位置变异策略丰富细菌种群的多样性,增强算法的全局搜索能力,同时设置算法的终止条件,避免输出功率来回振荡。仿真结果表明,该算法在多峰状态下能够更快速、更准确地实现最大功率点跟踪。
Aiming at the problem that the traditional maximum power point tracking algorithm is difficult to jump out of the local optimal value in the optimization process, the author proposes an improved bacterial foraging algorithm. Through adaptive adjustment of swimming step length and migration probability, improve the tracking speed and accuracy of the algorithm, introduce the global learning direction to strengthen the communication between bacterial populations, and use the position mutation strategy to enrich the diversity of bacterial populations, and enhance the global search ability of the algorithm, at the same time, set the termination condition of the algorithm to avoid the output power oscillating back and forth. The simulation results under the three conditions of uniform illumination, static shadow and dynamic shadow show that the proposed algorithm can achieve the maximum power point tracking faster and more accurately in the multi-peak state.
作者
高迎迎
朱武
董艺
花赟昊
GAO Yingying;ZHU Wu;DONG Yi;HUA Yunhao(College of Electronics and Information Engineering,Shanghai University of Electric Power,Shanghai 201306,China;State Grid Binzhou Power Supply Company,Binzhou Shandong 256600,China)
出处
《信息与电脑》
2022年第6期43-46,共4页
Information & Computer
基金
国网上海电力公司科技创新项目(项目编号:H2020-073)。
关键词
光伏阵列
最大功率点跟踪
改进细菌觅食算法
阴影遮挡
PV array
maximum power point tracking
improved bacterial foraging algorithm
shadow occlusion
