计算机应用 ›› 2012, Vol. 32 ›› Issue (12): 3299-3302.DOI: 10.3724/SP.J.1087.2012.03299

• 先进计算 • 上一篇    下一篇

粒子群优化鱼群算法及其在光伏系统最大功率点跟踪中的应用

段其昌1,唐若笠2,隆霞1   

  1. 1. 重庆大学 自动化学院,重庆400030
    2. 重庆大学 自动化学院, 重庆 400044
  • 收稿日期:2012-06-30 修回日期:2012-08-09 发布日期:2012-12-29 出版日期:2012-12-01
  • 通讯作者: 唐若笠
  • 作者简介:段其昌(1953-),男,四川自贡人,教授,博士,主要研究方向:网络优化、人工智能、新能源;〓唐若笠(1987-),男,湖北丹江口人,硕士研究生,主要研究方向:人工智能、新能源;〓隆霞(1988-),女,重庆人,硕士研究生,主要研究方向:图像处理、新能源。
  • 基金资助:
    重庆市重点科技攻关项目

Fish swarm algorithm optimized by PSO applied in maximum power point tracking of photovoltaic power system

DUAN Qi-chang1,TANG Ruo-li2,LONG Xia2   

  1. 1. College of Automation, Chongqing University, Chongqing 400030, China
    2. School of Automation, Chongqing University, Chongqing 400044, China
  • Received:2012-06-30 Revised:2012-08-09 Online:2012-12-29 Published:2012-12-01
  • Contact: TANG Ruo-li

摘要: 将标准粒子群优化算法中的速度惯性、粒子个体的记忆因素和粒子间学习交流因素等几个特征引入人工鱼群算法,提出了粒子群优化鱼群算法。在新算法中,鱼群的游动具有了速度惯性的特征,并且其行为模式被扩充为追尾、聚群、记忆、交流以及觅食。通过仿真分析,验证了粒子群优化鱼群算法比两种基本算法具有更快的收敛速度和更高的寻优精度,且性能稳定。最后将所提出的粒子群优化鱼群算法应用于局部遮阴情况下的光伏发电系统最大功率点跟踪,实验表明,该算法可以在很短时间内以很高精度寻得不均匀光照系统的最大功率点。

关键词: 粒子群算法, 人工鱼群算法, 行为模式, 光伏, 最大功率点跟踪

Abstract: Introducing the velocity inertia, memory capacity of each individual and learning or communicating capacity of Particle Swarm Optimization (PSO) into the Artificial Fish-Swarm Algorithm (AFSA), a new algorithm called the “Fish-Swarm Algorithm optimized by PSO(PSO-FSA)” was put forward. In this new algorithm, the swimming of each fish has velocity inertia, and the PSO-FSA has totally five kinds of behavior pattern as follows: swarming, following, remembering, communicating and searching. The simulation analysis shows that PSO-FSA has more stable and higher performance in convergence speed and searching precision than PSO and AFSA. Finally, the PSO-FSA was applied to the maximum power point tracking of photovoltaic power generation system under partially shaded condition, and the experimental results show that PSO-FSA can find the maximum power point under partially shaded insolation conditions quickly and precisely.

Key words: Particle Swarm Optimization (PSO), Artificial Fish Swarm Algorithm (AFSA), human activities, photovoltaic, maximum power point tracking

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