引用本文: | 陈前宇,陈维荣,戴朝华.电力系统无功优化多目标处理与算法改进[J].电力系统保护与控制,2014,42(5):129-135.[点击复制] |
CHEN Qian-yu,CHEN Wei-rong,DAI Chao-hua.Multi-objective reactive power optimization and improvement of particle swarm algorithm[J].Power System Protection and Control,2014,42(5):129-135[点击复制] |
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摘要: |
电力系统无功优化属于典型的多目标非线性复杂优化问题,求解非常困难。近年来,众多智能优化算法应用于该问题,其中粒子群优化(Particle Swarm Optimization, PSO)算法最具代表性;但PSO算法性能仍有待提高,如可能陷入局部极值。提出一种多策略融合粒子群优化(Particle Swarm Optimization with Multi-Strategy Integration,MSI-PSO)算法,对速度更新公式引入选择操作,分阶段加速因子调整和惯性权重动态调整,以平衡粒子局部搜索与全 |
关键词: 多目标无功优化 电压稳定 有功损耗 人工智能 多策略融合粒子群优化算法 |
DOI:10.7667/j.issn.1674-3415.2014.05.021 |
投稿时间:2013-06-03修订日期:2013-06-25 |
基金项目:国家自然科学基金(51307144) |
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Multi-objective reactive power optimization and improvement of particle swarm algorithm |
CHEN Qian-yu,CHEN Wei-rong,DAI Chao-hua |
(School of Electrical Engineering, Southwest Jiaotong University, Chengdu 610031, China) |
Abstract: |
Reactive power optimization is a typical multi-target nonlinear optimization problem, which is complex and difficult to solve. In recent years, many intelligent optimization algorithms are applied to solve the problem. The particle swarm optimization (PSO) algorithm is one of the most typical reactive power optimization intelligent optimization algorithms, while it still needs to be improved because it is easy to fall into local minima. This paper proposes an algorithm of particle swarm optimization with multi-strategy integration (MSI-PSO). Selection operation, phased adjustment of acceleration factor and the dynamic adjustment of inertia weight are introduced to the speed updating formula to balance the local and global search ability of particles. Some particles with poor performance are selected randomly to amend the individual cognitive part in the speed updating formula as social cognition to improve the accuracy and convergence speed of the particle search. Reactive power optimization simulation model is established with a target of minimum loss of the active network and maximum system voltage stability margin. The weighted method, membership function method and Pareto method are used to deal with the multi-objective problem. Simulation on the IEEE30 bus testing system is conducted. The results show that compared with several other improved PSO algorithms and the PSO algorithm based on Pareto optimal solution set, the proposed MSI-PSO algorithm has better performance and can effectively solve the multi-objective reactive power optimization. |
Key words: multi-objective reactive power optimization voltage stability active network loss artificial intelligence particle swarm optimization with multi-strategy integration algorithm |