摘要
粒子群优化算法是一种简便易行,收敛快速的演化计算方法。但该算法也存在收敛精度不高,易陷入局部极值的缺点。针对这些缺点,对原算法加以改进,引入了自适应的惯性系数和模拟退火算法的思想,提出了一种新的模拟退火粒子群优化(simulated annealing particle swarm optimization,SA-PSO)算法,并将其应用于电力系统无功优化。对IEEE14节点系统进行了仿真计算,并与PSO算法作了比较,结果表明SA-PSO算法全局收敛性能及收敛精度均较PSO算法有了较大提高。
Particle swarm optimization （PSO） is one af the evolutionary computation techniques which is convenient and has high convergence speed,but it also has some limitations such as premature convergence. So an improved method called simulated annealing particle swarm optimization （SA-PSO）algorithm is presented and is applied to reactive power optimization 0f power system,which takes advantage of the selfadaptation inertia weight coefficient and the idea of simulated annealing algorithm. The proposed method has significant improvement in global convergence property and convergence precision compared with PSO algorithm,which is proved by the simulation results of IEEE 14-node system.
出处
《电力系统及其自动化学报》
CSCD
北大核心
2007年第5期114-118,共5页
Proceedings of the CSU-EPSA
关键词
电力系统
无功优化
模拟退火粒子群优化算法
自适应
power system
reactive power optimization
simulated annealing particle swarm optimization algorithm （SA-PSO）
self-adaptation
