广东工业大学学报 ›› 2014, Vol. 31 ›› Issue (2): 85-89.doi: 10.3969/j.issn.1007-7162.2014.02.016

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基于组合混沌序列动态粒子群算法的电力系统无功优化

郭经韬,陈璟华,周俊,许伟龙   

  1. 广东工业大学 自动化学院,广东 广州 510006
  • 出版日期:2014-06-06 发布日期:2018-06-12
  • 作者简介:郭经韬(1988),男,硕士研究生,主要研究方向为电力系统安全运行与控制.
  • 基金资助:
    广东省电力节能与新能源技术重点实验室资助项目(ZDSYS200701)

Reactive Power Optimization Based on Combined Chaotic #br# Dynamic Particle Swarm Optimization Algorithm#br# 

Guo Jingtao, Chen Jinghua, Zhou Jun, Xu Weilong   

  1. School of Automation, Guangdong University of Technology, Guangzhou 510006, China
  • Online:2014-06-06 Published:2018-06-12
  • Supported by:
     

摘要: 无功优化对提高电力系统的安全性和稳定性具有重要意义.针对传统粒子群算法在求解大规模、强非线性无功优化时易陷入早熟、局部收敛等问题,应用Logistic混沌优化方法,充分利用其遍历性进行寻优.另外,为保障粒子群算法初值的均匀性,结合Chebyshev映射和Logistic映射,引入一种组合混沌映射并将其应用于粒子初始化,提高初始变量的均匀性,从而提高算法全局寻优能力.对粒子群速度更新过程中存在的惯性取值问题,引入一种基于种群速度的动态惯性权重策略.最后将这一算法应用于电力系统无功优化.算例表明,算法具有较强的全局搜索能力和较高的效率.

关键词: 混沌粒子群优化算法, 组合混沌序列, 动态惯性权重, 无功优化

Abstract: It is of great significance for the reactive power optimization algorithm to improve the security and stability of the power system. To avoid the disadvantages of the traditional particle swarm optimization algorithm, prematurity and local convergence,  while optimizing largescale, strong nonlinear reactive power, the logistic chaotic map, for its advanced searching ability, was applied in the optimization variable process of this algorithm. Besides, a chaotic sequence combining Chebyshev chaotic map with logistic chaotic map was applied to obtain a uniform initial value so as to improve the global optimization ability of the algorithm. As to the problem of inertia weight, the algorithm gave a dynamic inertia weight, based on population velocity. The results show that it performs better in global searching ability and efficiency.

Key words: chaotic particle swarm optimization algorithm, combined chaotic sequence, dynamic inertia weight, reactive power optimization

中图分类号: 

  • TM714.3
[1] 陈璟华, 邱明晋, 郭经韬, 唐俊杰. 模糊熵权法和CCPSO算法的含风电场电力系统多目标无功优化[J]. 广东工业大学学报, 2018, 35(01): 35-40.
[2] 胡廷鹤,孟安波. 基于虚拟极值粒子群的电网无功优化研究[J]. 广东工业大学学报, 2012, 29(4): 49-53.
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