广东工业大学学报 ›› 2017, Vol. 34 ›› Issue (05): 22-28.doi: 10.12052/gdutxb.160149

• 综合研究 • 上一篇    下一篇

基于改进差分进化和粒子群混合算法的电力系统最优潮流计算

陈璟华, 邱明晋, 唐俊杰, 田明正, 谭耿锐   

  1. 广东工业大学 自动化学院, 广东 广州 510006
  • 收稿日期:2016-12-05 出版日期:2017-09-09 发布日期:2017-07-10
  • 作者简介:陈璟华(1974-),女,副教授,博士,主要研究方向为电力系统运行与优化.
  • 基金资助:
    广东省自然科学基金资助项目（S2013040013776）；广东省教育厅育苗工程项目（2013LYM_0019）

A Hybrid Algorithm Based on Improved Differential Evolution and Particle Swarm Optimization for Power System Optimal Power Flow Calculation

Chen Jing-hua, Qiu Ming-jin, Tang Jun-jie, Tian Ming-zheng, Tan Geng-rui   

  1. School of Automation, Guangdong University of Technology, Guangzhou 510006, China
  • Received:2016-12-05 Online:2017-09-09 Published:2017-07-10

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摘要： 针对电力系统最优潮流典型的非线性多峰值的非凸规划问题，提出一种将差分进化与粒子群优化算法结合在一起的混合优化算法.采用双种群进化策略，分别利用粒子群优化算法和差分进化算法进行寻优迭代，通过信息分享机制，使两个种群在寻优过程中协同进化.提出一种老化机制和精英改选机制，根据最优粒子的引导能力动态改变其寿命.在其引导能力不足时，采用一种多项式变异策略引入一个竞争个体与最优粒子竞争，使算法全局寻优能力得到加强.IEEE30节点系统仿真结果表明，算法收敛速度快、精度高，具有一定的有效性和可行性.

关键词: 差分进化算法, 粒子群优化算法, 最优潮流, 混合算法

Abstract: A hybrid optimization algorithm combining differential evolution and particle swarm optimization is proposed to solve the non-convex programming problem of nonlinear optimal multi-peak of power system optimal power flow. By using the two-species evolutionary strategy, the particle swarm optimization algorithm and the differential evolution algorithm are used to optimize iterations. The information sharing mechanism is used to co-evolve the two populations in the process of optimization. An aging mechanism and a mechanism of elite selection are proposed to dynamically change the lifetime of the particle according to its guiding ability. In the case of lack of bootstrap capability, a polynomial mutation strategy is adopted to produce a competitor to compete with the optimal particle, so that the global optimization ability of the algorithm is enhanced. Simulation results show that the algorithm has high convergence speed and high precision, and it is effective and feasible.

Key words: differential evolution algorithm, particle swarm optimization algorithm, optimal power flow, hybrid algorithm

中图分类号: 

  • TM744
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