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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Vydáno v:	IEEE journal of selected topics in signal processing Ročník 12; číslo 4; s. 578 - 592
Hlavní autoři:	Zhao, Junbo, Mili, Lamine
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York IEEE 01.08.2018 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	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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Abstract	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.
AbstractList	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.
Author	Zhao, Junbo Mili, Lamine
Author_xml	– sequence: 1 givenname: Junbo orcidid: 0000-0002-8498-9666 surname: Zhao fullname: Zhao, Junbo email: zjunbo@vt.edu organization: Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Northern Virginia Center, Falls Church, VA, USA – sequence: 2 givenname: Lamine orcidid: 0000-0001-6134-3945 surname: Mili fullname: Mili, Lamine email: lmili@vt.edu organization: Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Northern Virginia Center, Falls Church, VA, USA
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Cites_doi	10.1109/TPWRS.2002.805016 10.1049/iet-rsn.2009.0170 10.1007/978-1-4612-5604-5 10.1002/0471725250 10.1109/TSG.2016.2548244 10.1109/TAC.2002.800742 10.1109/TPWRS.2017.2679121 10.1109/TPWRS.2018.2790390 10.1109/TSG.2017.2761452 10.1109/TSP.2009.2039731 10.1109/9.847726 10.1109/TPWRS.2011.2145396 10.1109/PESGM.2015.7286337 10.1016/j.epsr.2013.05.016 10.1109/TPWRS.2014.2331317 10.1109/TPWRS.2013.2251482 10.1214/aos/1032181171 10.1109/TPWRS.2014.2347047 10.1080/01621459.1992.10476254 10.1049/iet-smt.2011.0169 10.1109/TSG.2014.2345698 10.1109/TPWRS.2016.2628344 10.1109/TPWRS.2017.2785344 10.1016/j.dsp.2015.09.004 10.1109/LSP.2017.2669238 10.1109/59.780909 10.1109/59.910791 10.1109/TSP.2007.894262 10.1029/94JC00572 10.1109/59.496203 10.1109/TPWRS.2013.2281323 10.1109/TPWRD.2017.2762927 10.1109/TPWRS.2011.2175255
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References	ref13 ref12 hampel (ref33) 1986 ref37 ref15 ref14 ref31 ref30 ref11 ref32 ref10 ref2 ref1 ref17 ref38 ref16 huang (ref5) 0 ref19 ref18 ref24 ref23 ref26 evensen (ref21) 1994; 99 ref25 ref20 ref22 (ref35) 2015 ref28 ref27 ref29 ref8 ref7 (ref36) 2003 ref9 ref4 ref3 ref6 sauer (ref34) 1998
References_xml	– ident: ref3 doi: 10.1109/TPWRS.2002.805016 – ident: ref22 doi: 10.1049/iet-rsn.2009.0170 – start-page: 376 year: 0 ident: ref5 article-title: Feasibility studies of applying Kalman filter techniques to power system dynamic state estimation publication-title: Proc IEEE 8th Int Power Engineering Conf – ident: ref32 doi: 10.1007/978-1-4612-5604-5 – year: 1998 ident: ref34 publication-title: Power System Dynamics and Stability – ident: ref30 doi: 10.1002/0471725250 – year: 2015 ident: ref35 article-title: Benchmark systems for small-signal stability analysis and control publication-title: IEEE PES Piscataway – ident: ref10 doi: 10.1109/TSG.2016.2548244 – ident: ref28 doi: 10.1109/TAC.2002.800742 – ident: ref1 doi: 10.1109/TPWRS.2017.2679121 – ident: ref14 doi: 10.1109/TPWRS.2018.2790390 – ident: ref26 doi: 10.1109/TSG.2017.2761452 – ident: ref13 doi: 10.1109/TSP.2009.2039731 – ident: ref27 doi: 10.1109/9.847726 – ident: ref4 doi: 10.1109/TPWRS.2011.2145396 – ident: ref11 doi: 10.1109/PESGM.2015.7286337 – ident: ref6 doi: 10.1016/j.epsr.2013.05.016 – ident: ref19 doi: 10.1109/TPWRS.2014.2331317 – ident: ref17 doi: 10.1109/TPWRS.2013.2251482 – ident: ref38 doi: 10.1214/aos/1032181171 – ident: ref15 doi: 10.1109/TPWRS.2014.2347047 – ident: ref37 doi: 10.1080/01621459.1992.10476254 – ident: ref23 doi: 10.1049/iet-smt.2011.0169 – year: 2003 ident: ref36 article-title: Modeling of gas turbines and steam turbines in combined cycle power plants – ident: ref20 doi: 10.1109/TSG.2014.2345698 – ident: ref7 doi: 10.1109/TPWRS.2016.2628344 – ident: ref18 doi: 10.1109/TPWRS.2017.2785344 – ident: ref25 doi: 10.1016/j.dsp.2015.09.004 – year: 1986 ident: ref33 publication-title: Robust Statistics The Approach Based on Influence Functions – ident: ref24 doi: 10.1109/LSP.2017.2669238 – ident: ref16 doi: 10.1109/59.780909 – ident: ref2 doi: 10.1109/59.910791 – ident: ref31 doi: 10.1109/TSP.2007.894262 – volume: 99 start-page: 143 year: 1994 ident: ref21 article-title: Sequential data assimilation with a nonlinear quasigeostrophic model using Monte Carlo methods to forecast error statistics publication-title: J Geophys Res doi: 10.1029/94JC00572 – ident: ref29 doi: 10.1109/59.496203 – ident: ref8 doi: 10.1109/TPWRS.2013.2281323 – ident: ref12 doi: 10.1109/TPWRD.2017.2762927 – ident: ref9 doi: 10.1109/TPWRS.2011.2175255
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SubjectTerms	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
Title	A Robust Generalized-Maximum Likelihood Unscented Kalman Filter for Power System Dynamic State Estimation
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