A New State Estimation Model of Utilizing PMU Measurements
A New State Estimation Model of Utilizing PMU Measurements
复制标题
利用 PMU 测量的新状态估计模型
DOI：
10.1109/icpst.2006.321443
复制
发表时间：
2006
期刊：
影响因子：
--
通讯作者：
H. Zhao
中科院分区：
文献类型：
--
作者：
H. Zhao
PMU可以测量PMU节点的电压相量和相关支路的电流相量，并且通过PMU测量可以计算出相关节点的电压相量，因此这些节点的状态变量是已知的。利用PMU测量进行状态估计(SE)的研究有两种方法：一是将PMU测量视为正常测量，与SCADA测量一起用于非线性状态估计；另一种是仅使用 PMU 测量的线性状态估计，前提是系统完全可以用 PMU 进行观测。前者没有考虑PMU测量状态变量的能力，后者目前很难实现。考虑到PMU测量精度高、可靠性高等特点，本文提出了一种新的模型，将PMU测量或计算的状态变量作为状态估计中节点的状态变量。该模型可以利用PMU测量状态变量的能力，减少估计量的规模。通过仿真分析了新模型在计算耗时和收敛速度方面的改进。在新的SE模型中，假设PMU测量的权重为无穷大，并且不等于测量误差的方差倒数，因此状态估计精度受到PMU测量的权重偏差的影响。论文对新模型进行了更深入的分析和扩展，提出了一种双状态估计模型：首先以PMU测量或计算的状态变量作为节点状态变量进行非线性SE，然后利用非线性SE结果和PMU测量值进行线性SE。该模型增加的计算时间很少，但同时提高了SE方程的收敛速度和估计精度。通过仿真验证了双状态估计模型的有效性。
The PMU can measure voltage phasors of the PMU node and current phasors of the correlative branch, and voltage phasors of the correlative nodes can be calculated by PMU measurements, so the state variable of these nodes are known. Two ways of research on the state estimation(SE) with PMU measurements are: one regards the PMU measurements as normal measurements and uses them together with SCADA measurements in the nonlinear state estimation; the other is linear state estimation with only PMU measurements on the condition that the system is observable fully with PMUs. The former has not considered the PMU's ability of measuring state variables, and the latter is difficult to realize now. Considering the high measurement precision and reliability of the PMU, the paper gives a new model to regard the state variable measured or calculated by PMU as the state variable of nodes in the state estimation. The model can utilize the PMU's ability of measuring state variables, reduce the scale of estimator. The improvement of calculating time- consuming and convergence speed in the new model is analyzed by the simulation. In the new SE model, the weight of PMU measurements is assumed to be infinite, and not equal to the variance reciprocal of measurements error, so the state estimation precision is influenced by the weight departure of the PMU measurements. The paper analyses and expands the new model more deeply, and presents a double state estimation model: at first, the nonlinear SE is done on the condition of taking the state variable measured or calculated by PMU as the state variable of nodes, then the linear SE is done with both nonlinear SE results and PMU measurements. The model adds little calculation time, but improves the SE equations' convergence speed and estimation precision simultaneously. The double state estimation model is validated by the simulation.