电力系统保护与控制2011，Vol.39Issue(9)：26-31,6.
基于自适应混沌粒子群优化算法的多目标无功优化
Multi-objective reactive power optimization based on adaptive chaos particle swarm optimization algorithm
李娟 1杨琳 1刘金龙 1杨德龙 2张晨2
作者信息
- 1. 东北电力大学电气工程学院,吉林吉林132012
- 2. 华北电力大学电气与电子工程学院,北京102206
- 折叠
摘要
Abstract
Particle swarm algorithm used in reactive power optimization always falls into local optimal solution and final slow convergence due to generating particles as controlling variable values randomly. Consequently, by integrating the chaotic opitimization algorithm into the particle swarm algorithm, a new adaptive chaotic particle swarm optimization based on chaos theory is adopted to solve the problem. Through the using of chaos ergodicity furstly, the control variables in the system are initialized to enhance the diversity of particle populations. For each iteration update of the group, the individual particle's fitness value, namely the reactive power optimization objective function value is calculated, and according to their sizes some particles are selected to be treated with chaos optimization to help the reactive power optimization controlling variables to jump out of the local extreme regions;according to each particle's fitness value, its inertia weigh coefficient is adjusted adaptively to enhance the entire group of global and local search capabilities. Through calculation and analysis of cases, the results show that adaptive chaotic particle swarm algorithm used in reactive power optimization can jump out of local optimum in time to find the global optimal solution and complete fast convergence.关键词
自适应/混沌粒子群优化算法/无功优化/惯性权重Key words
adaptive/ chaotic particle swarm optimization algorithm/ reactive power optimization/ inertia weight分类
信息技术与安全科学引用本文复制引用
李娟,杨琳,刘金龙,杨德龙,张晨..基于自适应混沌粒子群优化算法的多目标无功优化[J].电力系统保护与控制,2011,39(9):26-31,6.
