Dynamic State Estimation in Power Systems Using Kalman Filters

被引：0
|
作者：
Tebianian, Hamed [1 ]
Jeyasurya, Benjamin [1 ]
机构：
[1] Mem Univ Newfoundland, Fac Engn & Appl Sci, St John, NF A1B 3X5, Canada
来源：
2013 IEEE ELECTRICAL POWER & ENERGY CONFERENCE (EPEC) | 2013年
关键词：
Power system state estimation; Extended Kalman Filter; Unscented Kalman Filter;
D O I：
暂无
中图分类号：
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号：
0807 ; 0820 ;
摘要：
Dynamic state estimation of power systems is necessary for wide area control purposes. Among the states of synchronous machine, precise, accurate, and timely information about rotor angle and speed deviation can be useful to enhance power system reliability and stability. In this paper two popular nonlinear estimation approaches: Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) are used to estimate main states of a simple power system using high rate data provided by Phasor Measurement Unit (PMU). A case study using a simple power system model is presented to illustrate the effectiveness of proposed approaches.
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页数：5
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