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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Veröffentlicht in:IEEE journal of selected topics in signal processing Jg. 12; H. 4; S. 578 - 592
Hauptverfasser: Zhao, Junbo, Mili, Lamine
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.08.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
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
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Zusammenfassung: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 using phasor measurement units (PMUs) follow long-tailed probability distributions, the conventional UKF provides strongly biased state estimates since it relies on the weighted least squares estimator. By contrast, the state estimates and residuals of our GM-UKF are proved to be roughly Gaussian, allowing the sigma points to reliably approximate the mean and the covariance matrices of the predicted and corrected state vectors. To develop our GM-UKF, we first derive a batch-mode regression form by processing the predictions and observations simultaneously, where the statistical linearization approach is used. We show that the set of equations so derived are equivalent to those of the unscented transformation. Then, a robust GM-estimator that minimizes a convex Huber cost function while using weights calculated via projection statistics (PSs) is proposed. The PSs are applied to a two-dimensional matrix that consists of a serially correlated predicted state and innovation vectors to detect observation and innovation outliers. These outliers are suppressed by the GM-estimator using the iteratively reweighted least squares algorithm. Finally, the asymptotic error covariance matrix of the GM-UKF state estimates is derived from the total influence function. Extensive simulation results carried out on IEEE New England 39-bus 10-machine test system verify the effectiveness and robustness of the proposed method.
Bibliographie:ObjectType-Article-1
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ISSN:1932-4553
1941-0484
DOI:10.1109/JSTSP.2018.2827261