The precision of state estimation of hybrid measuring system based on Wide-area Measurement system (WAMS) and Supervisory Control and Data Acquisition (SCADA) has been studied in this paper.
研究了广域量测系统与数据采集与监控系统组成的混合量测系统下的状态估计精度问题。
When using Kalman Filter to track a target, estimation of the initial state of the target is an important factor influencing tracking precision in the initial phase.
采用卡尔曼滤波器对目标进行跟踪时，目标初始状态估计是影响初始阶段跟踪精度的一个重要原因。
Furthermore, the precision improvement of state estimation due to incorporation of PMU is analyzed based on the model and particle swarm optimization (PSO) is applied to solve the placement of PMU.
基于此模型，本文还进一步分析了引入pmu后对状态估计精度的影响，从而提出了基于粒子群优化算法(PSO)的PMU配置。
State estimation models are developed with PMU of different precision.
并针对不同的相角量测精度，采用不同的状态估计模型。
In this paper, to improve the precision of power system state estimation, a mathematical model is proposed in which the PMU data and SCADA data coexist.
文中提出了一种pmu与scada数据共存的数学模型用于电力系统状态估计。
Based on the model, the precision improvement of state estimation due to incorporation of PMUs can be analyzed quantitatively and the scheme of PMU placement can be established.
基于此模型，可定量地分析引入PMU以后对状态估计精度的改善程度。进而讨论了以提高状态估计精度为目标的PMU配置方案。
Based on the model, the precision improvement of state estimation due to incorporation of PMUs can be analyzed quantitatively and the scheme of PMU placement can be established.
基于此模型，可定量地分析引入PMU以后对状态估计精度的改善程度。进而讨论了以提高状态估计精度为目标的PMU配置方案。
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