Application of parallel particle swarm optimization on power system state estimation
Application of parallel particle swarm optimization on power system state estimation
复制标题
并行粒子群优化在电力系统状态估计中的应用
DOI：
--
复制
发表时间：
2009
期刊：
影响因子：
--
通讯作者：
Juneho Park
中科院分区：
文献类型：
--
作者：
hee;Hwa;Juneho Park
在电力系统运行中，状态估计在安全控制中起着重要的作用。对于状态估计问题，目前广泛使用的是加权最小二乘法。然而，这些算法可以收敛到局部最优解。近年来，为了克服经典优化问题的缺点，人们引入了粒子群算法等现代启发式优化方法。然而，基于种群的启发式优化方法需要较长的计算时间才能找到最优解。本文将粒子群优化算法(PSO)用于电力系统状态估计的最优解搜索。针对启发式优化方法的不足，提出了基于PC集群系统的并行PSO算法。该方法在IEEE-118节点系统上进行了测试。仿真结果表明，基于PC机群系统的并行粒子群算法可以应用于电力系统状态估计。
In power system operations, state estimation plays an important role in security control. For the state estimation problem, the weighted least squares (WLS) method is widely used at present. However, these algorithms can converge to local optimal solutions. Recently, modern heuristic optimization methods such as Particle Swarm Optimization (PSO) have been introduced to overcome the disadvantage of the classical optimization problem. However, heuristic optimization methods based on populations require a lengthy computing time to find an optimal solution. In this paper, we used particle swarm optimization (PSO) to search for the optimal solution of state estimation in power systems. To overcome the shortcoming of heuristic optimization methods, we proposed parallel processing of the PSO algorithm based on the PC cluster system. The proposed approach was tested with the IEEE-118 bus systems. From the simulation results, we found that the parallel PSO based on the PC cluster system can be applicable for power system state estimation.