改进混沌PSO算法的电力系统最优潮流计算

电力工业的市场化改革对最优潮流(Optimal Power Flow,OPF)的计算精度和速度提出了更高的要求。在分析最优潮流理论及其算法的基础上,对比一些经典解算法,引入粒子群优化算法(PSO)来计算发电厂成本耗费问题。考虑到传统PSO算法处理OPF约束条件时,对随机粒子个体的质量和速度的选取不能保证,且收敛速度慢,并容易陷入局部最优解,提出改进的混沌粒子群算法,即利用混沌运动特性来改进粒子群算法。利用该算法与其他算法对IEEE5节点算例进行分析比较,结果表明改进的混沌微粒群优化算法可较好处理最优潮流约束条件,有效提高了PSO算法的全局收敛能力和计算精度。在处理最优潮流问题上具有一定的有效性和优越性。

Improved chaos PSO algorithm for optimal power flow calculation system

The market revolution of the power industry presents higher requirement for the computing precision and speed o Optimal Power Flow(OPF).Contrasting to some of the classical algorithm in the past,it introduces the Particle Swarm Optimization(PSO) to deal with the fuel cost of the generating company in the analysis of optimal power flow theory and on the basis of the algorithm.But when taken into account the traditional PSO algorithm processing OPF restrictive conditions,the random particle can not guarantee ...