The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
Unscented Kalman filter for power system dynamic state estimation
Abstract
A new estimation method for power system dynamic state estimation, the unscented Kalman filter (UKF), is presented. It is based on the application of the unscented transformation (UT) combined with the Kalman filter theory. One of the challenges in the process of power system estimation is coping with a highly non-linear mathematical model of network equations, which is usually approximated through a linearisation. The new derivative free estimation method overcomes this limitation using the UT and achieves better accuracy with simpler implementation. The UKF is derived and demonstrated using three different test power systems under typical network and measurement conditions. Its performance is compared with the classical extended Kalman filter. The simplicity of the new estimator and its low computational demand make it a better option to be applied in the next generation of dynamic system estimators.
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Cites
Unscented Filtering and Nonlinear Estimation
Julier, S. J., & Uhlmann, J. K. (2004) · Proceedings of the IEEE · doi:10.1109/jproc.2003.823141 · Cited by 6,588
Optimal State Estimation: Kalman, H∞, and Nonlinear Approaches
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A new method for the nonlinear transformation of means and covariances in filters and estimators
Julier, S., Uhlmann, J., & Durrant-Whyte, H. F. (2000) · IEEE Transactions on Automatic Control · doi:10.1109/9.847726 · Cited by 3,735
Optimal state estimation: Kalman, H [infinity], and nonlinear approaches
(2007) · Choice Reviews Online · doi:10.5860/choice.44-3334 · Cited by 2,108
The square-root unscented Kalman filter for state and parameter-estimation
Van der Merwe, R., & Wan, E. A. (2002) · 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221) · doi:10.1109/icassp.2001.940586 · Cited by 1,163
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On Unscented Kalman Filtering for State Estimation of Continuous-Time Nonlinear Systems
Sarkka, S. (2007) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2007.904453 · Cited by 553
Applying the unscented Kalman filter for nonlinear state estimation
Kandepu, R., Foss, B., & Imsland, L. (2008) · Journal of Process Control · doi:10.1016/j.jprocont.2007.11.004 · Cited by 495
Dynamic state estimation including anomaly detection and identification for power systems
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Cited by
Huang, Y.-F., Werner, S., Huang, J., Kashyap, N., & Gupta, V. (2012) · IEEE Signal Processing Magazine · doi:10.1109/msp.2012.2187037 · Cited by 450
Liu, X., Qian, C., Hatcher, W. G., Xu, H., Liao, W., & Yu, W. (2019) · IEEE Access · doi:10.1109/access.2019.2920763 · Cited by 152
Metadata
- Authors
- Valverde, G., & Terzija, V.
- Venue
- IET Generation, Transmission & Distribution
- Published
- 2011-01-07
- Volume
- 5
- Issue
- 1
- Pages
- 29-37
- ISSN
- 1751-8687 (ISSN-L)
- 1751-8695
- OpenAlex
- W2043776058
- Cited by
- 383
- References
- 12
- FWCI
- 8.97
- Type
- journal article
- Language
- en
- Open access
- closed
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