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...

Celý popis

Uloženo v:

Podrobná bibliografie
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
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

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
BookMark	eNp9kE1P4zAQhq0VSEDhD7AXS5zTHX-kcY4IKLtQREXhHLnOWBgSG2xXbPn1pBTtgcOe5j3MMx_PAdnxwSMhxwzGjEH962pxv5iPOTA15opXfMJ-kH1WS1aAVHJnkwUvZFmKPXKQ0hNAWU2Y3CfulN6F5Spleokeo-7cO7bFjf7r-lVPZ-4ZO_cYQksffDLoM7b0Wne99nTquoyR2hDpPLwNabFOGXt6vva6d4Yuss5IL1J2vc4u-EOya3WX8OirjsjD9OL-7Hcxu738c3Y6Kwyvy1xgC5UxBiUKaRWzoEuA1khlAbgyMOGSSb0sFaKtgRnQcoLLWrcGrRUViBE52c59ieF1hSk3T2EV_bCy4aBYXVa8roYute0yMaQU0TbG5c87c9Suaxg0G7HNp9hmI7b5Ejug_Bv6Eocf4_r_0M8t5BDxH6CEUExI8QGaBYgH
CODEN	IJSTGY
CitedBy_id	crossref_primary_10_1016_j_epsr_2025_111419 crossref_primary_10_1109_TPWRS_2020_2965795 crossref_primary_10_1109_TPWRS_2019_2894769 crossref_primary_10_1002_tee_24229 crossref_primary_10_1016_j_ijepes_2020_105962 crossref_primary_10_1016_j_sigpro_2024_109838 crossref_primary_10_1017_aer_2025_10069 crossref_primary_10_3390_axioms12111040 crossref_primary_10_3390_jmse12071095 crossref_primary_10_3390_en13102456 crossref_primary_10_1016_j_ijepes_2020_106412 crossref_primary_10_3390_act12020070 crossref_primary_10_3390_electronics13010114 crossref_primary_10_1109_TCBB_2022_3173969 crossref_primary_10_1109_TPWRS_2020_3028047 crossref_primary_10_1109_TSG_2019_2957799 crossref_primary_10_1109_JSYST_2019_2936595 crossref_primary_10_1016_j_sigpro_2023_109271 crossref_primary_10_3390_electronics14153059 crossref_primary_10_1049_iet_gtd_2018_7110 crossref_primary_10_1109_TPWRS_2023_3263203 crossref_primary_10_1049_sil2_12075 crossref_primary_10_3390_electronics13112177 crossref_primary_10_1109_TIV_2024_3429331 crossref_primary_10_1016_j_neucom_2018_10_051 crossref_primary_10_1109_LSP_2018_2879453 crossref_primary_10_1109_JAS_2023_123795 crossref_primary_10_1049_rpg2_12851 crossref_primary_10_1109_TVT_2022_3163207 crossref_primary_10_1016_j_isatra_2022_02_047 crossref_primary_10_1109_TPWRS_2019_2939098 crossref_primary_10_1109_TII_2019_2917940 crossref_primary_10_1016_j_asr_2025_06_002 crossref_primary_10_1016_j_jtice_2020_04_009 crossref_primary_10_1109_ACCESS_2020_3031978 crossref_primary_10_1109_TMECH_2024_3435128 crossref_primary_10_1016_j_sigpro_2021_108150 crossref_primary_10_3390_s24072351 crossref_primary_10_1109_TIM_2022_3157005 crossref_primary_10_1016_j_sigpro_2021_108394 crossref_primary_10_1109_TPWRS_2019_2945778 crossref_primary_10_1016_j_jprocont_2022_11_013 crossref_primary_10_1109_TSP_2019_2908910 crossref_primary_10_1109_LSP_2020_2973116 crossref_primary_10_1109_TNSE_2021_3093535 crossref_primary_10_1109_TIM_2021_3137553 crossref_primary_10_3390_electronics13234727 crossref_primary_10_1109_TII_2022_3165890 crossref_primary_10_1109_JSYST_2022_3197426 crossref_primary_10_1002_2050_7038_12714 crossref_primary_10_1109_JSEN_2022_3143885 crossref_primary_10_1016_j_ijepes_2022_108898 crossref_primary_10_1109_TIM_2022_3189743 crossref_primary_10_1109_TIM_2022_3160562 crossref_primary_10_1109_TIM_2021_3079556 crossref_primary_10_1088_1361_6501_ace643 crossref_primary_10_1016_j_ijepes_2024_109948 crossref_primary_10_1109_JSYST_2022_3171751 crossref_primary_10_1109_TIM_2025_3550601 crossref_primary_10_1109_MSP_2021_3134635 crossref_primary_10_1109_TPWRS_2019_2909000 crossref_primary_10_1109_TSG_2018_2870327 crossref_primary_10_1109_ACCESS_2025_3529853 crossref_primary_10_1109_LSP_2022_3221852 crossref_primary_10_1109_TAES_2023_3266176 crossref_primary_10_1007_s00773_020_00763_0 crossref_primary_10_1088_1361_6501_accebe crossref_primary_10_1109_TIM_2024_3502877 crossref_primary_10_1109_TPWRS_2021_3079395 crossref_primary_10_1109_ACCESS_2021_3092748 crossref_primary_10_1109_LSENS_2022_3225235 crossref_primary_10_3390_s25061741 crossref_primary_10_1016_j_robot_2020_103661 crossref_primary_10_1016_j_epsr_2023_109123 crossref_primary_10_1109_TPWRS_2019_2939192 crossref_primary_10_1016_j_epsr_2022_108370 crossref_primary_10_1109_JIOT_2024_3351850 crossref_primary_10_1109_LCSYS_2021_3068703 crossref_primary_10_1016_j_segan_2022_100712 crossref_primary_10_1049_iet_gtd_2018_5074 crossref_primary_10_1109_TIM_2020_2999757 crossref_primary_10_1109_TPWRS_2019_2953256 crossref_primary_10_1109_JAS_2021_1004350 crossref_primary_10_1109_TSTE_2021_3118030 crossref_primary_10_23919_PCMP_2023_000307 crossref_primary_10_3390_app14093952 crossref_primary_10_1016_j_jfranklin_2020_08_012 crossref_primary_10_1016_j_sigpro_2025_110008 crossref_primary_10_1049_stg2_12168 crossref_primary_10_1049_gtd2_12976 crossref_primary_10_1109_LSP_2019_2922620 crossref_primary_10_1016_j_epsr_2018_07_019 crossref_primary_10_1109_TII_2022_3202522 crossref_primary_10_3390_s20195459 crossref_primary_10_1109_TIM_2023_3328095
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
ContentType	Journal Article
Copyright	Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2018
Copyright_xml	– notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2018
DBID	97E RIA RIE AAYXX CITATION 7SP 8FD H8D L7M
DOI	10.1109/JSTSP.2018.2827261
DatabaseName	IEEE All-Society Periodicals Package (ASPP) 2005–Present IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE Electronic Library (IEL) CrossRef Electronics & Communications Abstracts Technology Research Database Aerospace Database Advanced Technologies Database with Aerospace
DatabaseTitle	CrossRef Aerospace Database Technology Research Database Advanced Technologies Database with Aerospace Electronics & Communications Abstracts
DatabaseTitleList	Aerospace Database
Database_xml	– sequence: 1 dbid: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher
DeliveryMethod	fulltext_linktorsrc
Discipline	Engineering
EISSN	1941-0484
EndPage	592
ExternalDocumentID	10_1109_JSTSP_2018_2827261 8338134
Genre	orig-research
GrantInformation_xml	– fundername: Advanced Grid Modeling program of the U.S. Department of Energy (DOE) Office of Electricity Delivery & Energy Reliability – fundername: National Science Foundation; U.S. National Science Foundation grantid: ECCS-1711191 funderid: 10.13039/100000001
GroupedDBID	-~X 0R~ 29I 4.4 5GY 5VS 6IK 97E AAJGR AARMG AASAJ AAWTH ABAZT ABQJQ ABVLG ACIWK AENEX AETIX AGQYO AGSQL AHBIQ AKJIK AKQYR ALMA_UNASSIGNED_HOLDINGS ATWAV BEFXN BFFAM BGNUA BKEBE BPEOZ CS3 DU5 EBS EJD F5P HZ~ IFIPE IPLJI JAVBF LAI M43 O9- OCL RIA RIE RNS AAYXX CITATION 7SP 8FD H8D L7M RIG
ID	FETCH-LOGICAL-c295t-ed07ccce4e34f81f0a500dc48f0028c062414ab58eef901c0a46eb9adceff3703
IEDL.DBID	RIE
ISICitedReferencesCount	118
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000440807600002&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	1932-4553
IngestDate	Mon Jun 30 10:20:16 EDT 2025 Sat Nov 29 04:10:32 EST 2025 Tue Nov 18 22:14:45 EST 2025 Wed Aug 27 08:31:00 EDT 2025
IsPeerReviewed	true
IsScholarly	true
Issue	4
Language	English
License	https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c295t-ed07ccce4e34f81f0a500dc48f0028c062414ab58eef901c0a46eb9adceff3703
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ORCID	0000-0002-8498-9666 0000-0001-6134-3945
PQID	2081957297
PQPubID	75721
PageCount	15
ParticipantIDs	ieee_primary_8338134 proquest_journals_2081957297 crossref_citationtrail_10_1109_JSTSP_2018_2827261 crossref_primary_10_1109_JSTSP_2018_2827261
PublicationCentury	2000
PublicationDate	2018-08-01
PublicationDateYYYYMMDD	2018-08-01
PublicationDate_xml	– month: 08 year: 2018 text: 2018-08-01 day: 01
PublicationDecade	2010
PublicationPlace	New York
PublicationPlace_xml	– name: New York
PublicationTitle	IEEE journal of selected topics in signal processing
PublicationTitleAbbrev	JSTSP
PublicationYear	2018
Publisher	IEEE The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher_xml	– name: IEEE – name: The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
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
SSID	ssj0057614
Score	2.5403185
Snippet	This paper develops a new robust generalized maximum-likelihood-type unscented Kalman filter (GM-UKF) that is able to suppress observation and innovation...
SourceID	proquest crossref ieee
SourceType	Aggregation Database Enrichment Source Index Database Publisher
StartPage	578
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
URI	https://ieeexplore.ieee.org/document/8338134 https://www.proquest.com/docview/2081957297
Volume	12
WOSCitedRecordID	wos000440807600002&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVIEE databaseName: IEEE Electronic Library (IEL) customDbUrl: eissn: 1941-0484 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0057614 issn: 1932-4553 databaseCode: RIE dateStart: 20070101 isFulltext: true titleUrlDefault: https://ieeexplore.ieee.org/ providerName: IEEE
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3PS8MwFH5M8aAHf4vTKTl402h_pE17HOoQ1DHUwW4lTV6huHXiOhH_epO0HYoieOshCaXfy8t7fXnfB3DC3MzJlKMo8lRQI69NBYs9mnq-MR8eBrwSm-D9fjQaxYMWnC16YRDRXj7Dc_Noa_lqKufmV9lFpPMp12dLsMR5WPVqNV5Xr-rWFWSPsiDwmwYZJ77QJv44MLe4onOdYHAvdL8dQlZV5YcrtudLb-N_b7YJ63UcSboV8FvQwmIb1r6wC-5A3iUP03Q-K0nNLZ1_oKL34j2fzCfkLn_GcW5IjcmwsJROqMitGE9EQXq5qaETHc-SgVFRIxWvObmq5OuJjVDJtfYOVePjLgx710-XN7RWVqDSi4OSonK4lBIZ-iyLNFxGF0FJFmUmB5NOqM91JtIgQsx0wCAdwUJMY6EkZpmvncQeLBfTAveBxK7gylQzeaqXk07su4HwIuEzJcMIwza4zadOZE07btQvxolNP5w4sfAkBp6khqcNp4s5LxXpxp-jdwwgi5E1Fm3oNIgm9b6c6XmmbqgTCn7w-6xDWDVrV1f8OrBcvs7xCFbkW5nPXo-tyX0COzzUFQ
linkProvider	IEEE
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LS8QwEB58gXrwLa7PHLxpNG3Tpj2Kuiiuy-IDvJU0mUJxtyvuroi_3iTtLooieOshSUu_dDLTmfk-gEPu5SzXTFMUmaRWXptKnvg08wO7fUQUikpsQrTb8dNT0pmC40kvDCK64jM8sZcul6_7amR_lZ3GJp7yAj4NsyHnPqu6tcZ216zr1Tlkn_IwDMYtMiw5NZv8vmPruOITE2IIP_K-HUNOV-WHMXYnTHP5f8-2Aku1J0nOKuhXYQrLNVj8wi-4DsUZuetno8GQ1OzSxQdqeivfi96oR1rFM3YLS2tMHktH6oSa3MhuT5akWdgsOjEeLelYHTVSMZuTi0rAnjgflVwa-1C1Pm7AY_Py4fyK1toKVPlJOKSomVBKIceA57EBzCojaMXj3EZhikXmZOcyC2PE3LgMikkeYZZIrTDPA2MmNmGm7Je4BSTxpNA2nykys5xiSeCF0o9lwLWKYowa4I1fdapq4nGrf9FNXQDCktTBk1p40hqeBhxN5rxUtBt_jl63gExG1lg0YHeMaFp_mQMzz2YOTUghtn-fdQDzVw-3rbR13b7ZgQV7n6rgbxdmhq8j3IM59TYsBq_7bvt9Amlk11w
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=A+Robust+Generalized-Maximum+Likelihood+Unscented+Kalman+Filter+for+Power+System+Dynamic+State+Estimation&rft.jtitle=IEEE+journal+of+selected+topics+in+signal+processing&rft.au=Zhao%2C+Junbo&rft.au=Mili%2C+Lamine&rft.date=2018-08-01&rft.issn=1932-4553&rft.eissn=1941-0484&rft.volume=12&rft.issue=4&rft.spage=578&rft.epage=592&rft_id=info:doi/10.1109%2FJSTSP.2018.2827261&rft.externalDBID=n%2Fa&rft.externalDocID=10_1109_JSTSP_2018_2827261
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1932-4553&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1932-4553&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1932-4553&client=summon