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

Journal Article · Thu Mar 01 04:00:00 EST 2018 · IEEE Transactions on Smart Grid

DOI:https://doi.org/10.1109/TSG.2016.2580584· OSTI ID:1429865

Qi, Junjian; Sun, Kai; Wang, Jianhui; Liu, Hui

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.

• Research Organization: Argonne National Laboratory (ANL)
• Sponsoring Organization: USDOE Office of Electricity Delivery and Energy Reliability
• DOE Contract Number: AC02-06CH11357
• OSTI ID: 1429865
• Journal Information: IEEE Transactions on Smart Grid, Journal Name: IEEE Transactions on Smart Grid Journal Issue: 2 Vol. 9; ISSN 1949-3053
• Publisher: IEEE
• Country of Publication: United States
• Language: English

Related Subjects

Extended Kalman filter
dynamic state estimation
nonlinear filters
nonlocal sampling effect
numerical stability
phasor measurement unit (PMU)
positive semidefinite
square-root unscented Kalman filter
synchrophasor
unscented Kalman filter