The PMU-Based Power System Dynamic State Estimation Using Extended Kalman Filter

被引：0
|
作者：
Jin, Xianing [1 ]
Wang, Guanqun [2 ]
Xue, Zhenyu [1 ]
Sun, Chongbo [1 ]
Song, Yi [1 ]
机构：
[1] State Power Econ Res Inst, State Grid, Beijing 102209, Peoples R China
[2] Washington State Univ, Dept Elect Engn & Comp Sci, Pullman, WA 99163 USA
来源：
PROCEEDINGS OF THE 2016 4TH INTERNATIONAL CONFERENCE ON MACHINERY, MATERIALS AND COMPUTING TECHNOLOGY | 2016年 / 60卷
关键词：
PMU; Dynamic State Estimation; Extended Kalman Filter; OBSERVABILITY; PLACEMENT;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Dynamic state estimation of power system is a sophisticated problem since voltage and current phasors under dynamic conditions are nonlinear and hard to be obtained. This paper presents a new power system dynamic state estimation method using Extended Kalman Filter (EKF) based on Phasor Measurement Unit (PMU). EKF can be used to deal with nonlinear system. With the help of PMU which is the key unit of Wide Area Measurement Systems (WAMS), continuous time waveforms with high accuracy and synchronized time stamps can be estimated. In case study, the effectiveness of the proposed method has been evaluated by dynamic state estimation of 3-bus powers system in Matlab, and scenarios with different PMU placement are compared. The proposed method achieves high accuracy in all these scenarios.
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页码：1185 / 1190
页数：6
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