【摘要】 针对当前电力系统中数据采集与监视控制系统(Supervisory Control And Date Acquisition,SCADA)和相量测量单元(Phasor Measurement Units,PMU)并存,以及不同规格的PMU之间不能实现完全同步的现状,引入了一种基于不完全同步PMU的电力系统动态状态估计模型,与SCADA组成混合量测系统,在此基础上提出了一种考虑相位失配量的线性动态状态估计算法.结合线性定常系统卡尔曼滤波原理,分别对相位失配量和系统状态进行滤波处理,实现对系统状态的估计.相比于传统方法,该算法的雅可比矩阵为常数阵,缩短了状态估计时间,减少了不必要的迭代.采用Matlab平台在IEEE 30节点系统上进行仿真实验,结果表明,在PMU不完全同步的情况下,提出的方法较传统的线性算法具有更好的性能,在很大程度上减小了状态估计产生的误差.更多还原

【Abstract】 Aiming at the coexistence of the supervisory control and data acquisition(SCADA)and phasor measurement units(PMU)available for power system,and inaccurate synchronization is an inevitable problem that large scale deployment of PMUs has to face.This paper introduces a model for power system state estimation using PMUs with phase mismatch to mix with the measurements of SCADA and presents a linear dynamic state estimation(DSE)algorithm considering phase mismatch.Phase mismatch and system state are processed using Kalman filter respectively in the algorithm to estimation the state.The algorithm has a constant Jacobian matrix,shortening the estimated reduction in computation time,and reducing unnecessary iterations.The simulation shows that,in the case of incomplete synchronization PMU,the method presented in this paper has better precision than the traditional linear algorithm,reducing the state estimation error largely.更多还原