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

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 (UKFGPS) is proposed and compared with five existing approaches, including UKFschol, UKF-kappa, UKFmodified, UKF-Delta Q, and the squareroot UKF (SRUKF). 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 UKFschol, UKF-kappa, and UKF-Delta Q do not work well in some estimations while UKFGPS works well in most cases. UKFmodified and SRUKF can always work well, indicating their better scalability mainly due to the enhanced numerical stability.

Authors:
ORCiD logo; ORCiD logo; ORCiD logo;
Publication Date:
Research Org.:
Argonne National Lab. (ANL), Argonne, IL (United States)
Sponsoring Org.:
USDOE Office of Electricity Delivery and Energy Reliability
OSTI Identifier:
1429865
DOE Contract Number:
AC02-06CH11357
Resource Type:
Journal Article
Resource Relation:
Journal Name: IEEE Transactions on Smart Grid; Journal Volume: 9; Journal Issue: 2
Country of Publication:
United States
Language:
English
Subject:
square-root unscented Kalman filter; Extended Kalman filter; dynamic state estimation; nonlinear filters; nonlocal sampling effect; numerical stability; phasor measurement unit (PMU); positive semidefinite; synchrophasor; unscented Kalman filter

Citation Formats

Qi, Junjian, Sun, Kai, Wang, Jianhui, and Liu, Hui. Dynamic State Estimation for Multi-Machine Power System by Unscented Kalman Filter With Enhanced Numerical Stability. United States: N. p., 2018. Web. doi:10.1109/TSG.2016.2580584.
Qi, Junjian, Sun, Kai, Wang, Jianhui, & Liu, Hui. Dynamic State Estimation for Multi-Machine Power System by Unscented Kalman Filter With Enhanced Numerical Stability. United States. doi:10.1109/TSG.2016.2580584.
Qi, Junjian, Sun, Kai, Wang, Jianhui, and Liu, Hui. Thu . "Dynamic State Estimation for Multi-Machine Power System by Unscented Kalman Filter With Enhanced Numerical Stability". United States. doi:10.1109/TSG.2016.2580584.
@article{osti_1429865,
title = {Dynamic State Estimation for Multi-Machine Power System by Unscented Kalman Filter With Enhanced Numerical Stability},
author = {Qi, Junjian and Sun, Kai and Wang, Jianhui and Liu, Hui},
abstractNote = {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 (UKFGPS) is proposed and compared with five existing approaches, including UKFschol, UKF-kappa, UKFmodified, UKF-Delta Q, and the squareroot UKF (SRUKF). 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 UKFschol, UKF-kappa, and UKF-Delta Q do not work well in some estimations while UKFGPS works well in most cases. UKFmodified and SRUKF can always work well, indicating their better scalability mainly due to the enhanced numerical stability.},
doi = {10.1109/TSG.2016.2580584},
journal = {IEEE Transactions on Smart Grid},
number = 2,
volume = 9,
place = {United States},
year = {Thu Mar 01 00:00:00 EST 2018},
month = {Thu Mar 01 00:00:00 EST 2018}
}