A Robust Generalized-Maximum Likelihood Unscented Kalman Filter for Power System Dynamic State Estimation

This paper develops a new robust generalized maximum-likelihood-type unscented Kalman filter (GM-UKF) that is able to suppress observation and innovation outliers while filtering out non-Gaussian process and measurement noise. Because the errors of the real and reactive power measurements calculated...

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Bibliographic Details
Published in:	IEEE journal of selected topics in signal processing Vol. 12; no. 4; pp. 578 - 592
Main Authors:	Zhao, Junbo, Mili, Lamine
Format:	Journal Article
Language:	English
Published:	New York IEEE 01.08.2018 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Computer simulation Covariance matrices Covariance matrix cyber attacks Data analysis Dynamic state estimation Estimates Filtration Forecasting Gaussian process Influence functions Innovations Kalman filters Least squares Mathematical analysis Matrix methods Noise measurement non-Gaussian noise outliers Outliers (statistics) Phasor measurement units Power measurement Power system dynamics Predictions Reactive power robust estimation Robustness Simulation State estimation State vectors Statistical analysis Technological innovation total influence function unscented Kalman filter
ISSN:	1932-4553, 1941-0484
Online Access:	Get full text
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