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首页> 外文会议>Transmission amp; Distribution Conference amp; Exposition: Asia and Pacific, 2009 >Application of parallel particle swarm optimization on power system state estimation
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Application of parallel particle swarm optimization on power system state estimation

机译：并行粒子群算法在电力系统状态估计中的应用

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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.
机译：在电力系统运行中，状态估计在安全控制中起着重要作用。对于状态估计问题，目前广泛使用加权最小二乘法（WLS）。但是，这些算法可以收敛到局部最优解。最近，为了克服经典优化问题的缺点，引入了现代启发式优化方法，例如粒子群优化（PSO）。但是，基于总体的启发式优化方法需要较长的计算时间才能找到最佳解决方案。在本文中，我们使用粒子群优化（PSO）搜索电力系统状态估计的最佳解决方案。为了克服启发式优化方法的缺点，我们提出了基于PC集群系统的PSO算法的并行处理。提议的方法已在IEEE-118总线系统上进行了测试。从仿真结果可以看出，基于PC集群系统的并行PSO可以用于电力系统状态估计。

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