An improved particle swarm optimization algorithm for reactive power optimization

Tuo Xie, Gang Zhang, Jiancang Xie, Yin Liu
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引用次数: 4

Abstract

Reactive power optimization of power system is a complicated multi-objective, multi-constraint combination optimization problem, particle swarm optimization (PSO) algorithm is the most commonly used algorithm to solve this problem. Aiming at the disadvantages of PSO algorithm, this paper came up with an improved particle swarm optimization (IPSO) algorithm. Firstly, it improved the particle population and initial position, and introduced weight coefficient in iterative process of evolution, which made the particles search process more reasonable and avoided premature convergence, secondly, it introduced the mutation operation to prevent particle swarming into local optimum, and enhanced the global optimization ability of the algorithm. Through the simulation calculation of the IEEE 6 nodes system, the results showed that IPSO algorithm is more effective than PSO algorithm.
一种改进的粒子群算法用于无功优化
电力系统无功优化是一个复杂的多目标、多约束组合优化问题,粒子群优化算法(PSO)是解决该问题最常用的算法。针对粒子群优化算法的不足,提出了一种改进的粒子群优化算法。首先,改进了粒子种群和初始位置,并在迭代进化过程中引入权系数,使粒子搜索过程更加合理,避免了过早收敛;其次,引入突变操作,防止粒子群陷入局部最优,增强了算法的全局寻优能力。通过对ieee6节点系统的仿真计算,结果表明IPSO算法比PSO算法更有效。
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