提出了蚁群算法在电力系统PMU布局优化中的应用

Bo Wang, Dichen Liu, Li Xiong
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引用次数: 6

摘要

基于gps的同步相量测量技术是保证互联电力系统安全可靠运行的有力工具。提出了一种基于蚁群算法的相量测量单元(PMU)布局优化方法。在蚁群系统(ACS)中引入信息素轨迹持续系数自适应调节机制和随机扰动进程,防止算法进入停滞行为,陷入局部极小值。将改进算法应用于PMU布局优化问题,在获得全局最优解和收敛速度上优于ACS算法。采用基于深度优先搜索的图论方法分析系统的可观测性。仿真结果表明,改进的ACS算法是有效的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Advance ACO system in optimizing power system PMU placement problem
GPS-based synchronous phasor measurement technology is a powerful tool for the security and reliable operation of the inter-connected electric power system. This paper presents an ACO-based approach to optimize the phasor measurement unit (PMU) placement problem. The pheromone trail persistence coefficient adaptive adjustment mechanism and stochastic perturbing progress are introduced into the Ant Colony System(ACS), in case the algorithm entering the stagnation behavior and getting stuck at local minima. The improved algorithm outperforms the ACS in obtaining global optimal solution and convergence speed, when applied to optimizing the PMU placement problem. A graph-theoretic procedure based on depth first search is adopted to analyze system observability. Simulation results in optimizing a provincial 46-bus system PMU placement problem show that the improved ACS algorithm is effective.
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