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Dynamic State Estimation for Multi-Machine Power System by Unscented Kalman Filter With Enhanced Numerical Stability

descriptionPublicationkeyboard_double_arrow_right Article , Preprint , Other literature type , Journal 01 Mar 2018Embargo end date: 01 Jan 2015Publisher:Institute of Electrical and Electronics Engineers (IEEE)Journal:IEEE Transactions on Smart Grid, volume 9, pages 1,184-1,196 (issn: 1949-3053, eissn: 1949-3061,

Authors: Junjian Qi; Kai Sun; Jianhui Wang; Hui Liu;

doi: 10.1109/tsg.2016.2580584 , 10.48550/arxiv.1509.07394

arXiv: 1509.07394 , http://arxiv.org/abs/1509.07394

Dynamic State Estimation for Multi-Machine Power System by Unscented Kalman Filter With Enhanced Numerical Stability

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Abstract

In this paper, in order to enhance the numerical stability of the unscented Kalman filter (UKF) used for power system dynamic state estimation, a new UKF with guaranteed positive semidifinite estimation error covariance (UKF-GPS) is proposed and compared with five existing approaches, including UKF-schol, UKF-$��$, UKF-modified, UKF-$��Q$, and the square-root unscented Kalman filter (SR-UKF). These methods and the extended Kalman filter (EKF) are tested by performing dynamic state estimation on WSCC 3-machine 9-bus system and NPCC 48-machine 140-bus system. For WSCC system, all methods obtain good estimates. However, for NPCC system, both EKF and the classic UKF fail. It is found that UKF-schol, UKF-$��$, and UKF-$��Q$ do not work well in some estimations while UKF-GPS works well in most cases. UKF-modified and SR-UKF can always work well, indicating their better scalability mainly due to the enhanced numerical stability.

accepted by IEEE Transactions on Smart Grid

Related Organizations

Guangxi University
China (People's Republic of)
Tennessee State University
United States
Argonne National Laboratory
United States
Guangxi University
China (People's Republic of)
University of Tennessee at Knoxville
United States

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Keywords

Optimization and Control (math.OC), FOS: Mathematics, Mathematics - Optimization and Control

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