Dynamic state estimation in power systems using Kalman filters
Dynamic state estimation of power systems is necessary for wide area control purposes. Among the states of synchronous machine, precise, accurate, and timely information about rotor angle and speed deviation can be useful to enhance power system reliability and stability. In this paper two popular n...
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| creator | Tebianian, Hamed Jeyasurya, Benjamin |
| description | Dynamic state estimation of power systems is necessary for wide area control purposes. Among the states of synchronous machine, precise, accurate, and timely information about rotor angle and speed deviation can be useful to enhance power system reliability and stability. In this paper two popular nonlinear estimation approaches: Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) are used to estimate main states of a simple power system using high rate data provided by Phasor Measurement Unit (PMU). A case study using a simple power system model is presented to illustrate the effectiveness of proposed approaches. |
| doi_str_mv | 10.1109/EPEC.2013.6802979 |
| format | Conference Proceeding |
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Among the states of synchronous machine, precise, accurate, and timely information about rotor angle and speed deviation can be useful to enhance power system reliability and stability. In this paper two popular nonlinear estimation approaches: Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) are used to estimate main states of a simple power system using high rate data provided by Phasor Measurement Unit (PMU). A case study using a simple power system model is presented to illustrate the effectiveness of proposed approaches.</description><identifier>EISBN: 9781479901067</identifier><identifier>EISBN: 1479901067</identifier><identifier>EISBN: 9781479901050</identifier><identifier>EISBN: 1479901059</identifier><identifier>DOI: 10.1109/EPEC.2013.6802979</identifier><language>eng</language><publisher>IEEE</publisher><subject>Equations ; Extended Kalman Filter ; Kalman filters ; Mathematical model ; Phasor measurement units ; Power system dynamics ; Power system stability ; Power system state estimation ; State estimation ; Unscented Kalman Filter</subject><ispartof>2013 IEEE Electrical Power & Energy Conference, 2013, p.1-5</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/6802979$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,2058,27925,54920</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/6802979$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Tebianian, Hamed</creatorcontrib><creatorcontrib>Jeyasurya, Benjamin</creatorcontrib><title>Dynamic state estimation in power systems using Kalman filters</title><title>2013 IEEE Electrical Power & Energy Conference</title><addtitle>EPEC</addtitle><description>Dynamic state estimation of power systems is necessary for wide area control purposes. Among the states of synchronous machine, precise, accurate, and timely information about rotor angle and speed deviation can be useful to enhance power system reliability and stability. In this paper two popular nonlinear estimation approaches: Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) are used to estimate main states of a simple power system using high rate data provided by Phasor Measurement Unit (PMU). A case study using a simple power system model is presented to illustrate the effectiveness of proposed approaches.</description><subject>Equations</subject><subject>Extended Kalman Filter</subject><subject>Kalman filters</subject><subject>Mathematical model</subject><subject>Phasor measurement units</subject><subject>Power system dynamics</subject><subject>Power system stability</subject><subject>Power system state estimation</subject><subject>State estimation</subject><subject>Unscented Kalman Filter</subject><isbn>9781479901067</isbn><isbn>1479901067</isbn><isbn>9781479901050</isbn><isbn>1479901059</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2013</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotj8tKxDAUQONCUMZ-gLjJD7TepG1u70aQWh84oAtdD0l7I5G2MzQR6d874KzO7nCOENcKCqWAbrv3ri00qLIwDWhCOhMZYaMqJAIFBi9EFuM3AChEVVdwKe4e1tlOoZcx2cSSYwqTTWE_yzDLw_6XFxnXmHiK8ieG-Uu-2nGys_RhTLzEK3Hu7Rg5O3EjPh-7j_Y53749vbT32zworFPu3QCuUQMZ0xx7ACt2Q9UbQs2krHPGetcTmpI064YMUs1V6Qevwfc1lxtx8-8NzLw7LMfIZd2dLss_K8tIRA</recordid><startdate>201308</startdate><enddate>201308</enddate><creator>Tebianian, Hamed</creator><creator>Jeyasurya, Benjamin</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201308</creationdate><title>Dynamic state estimation in power systems using Kalman filters</title><author>Tebianian, Hamed ; Jeyasurya, Benjamin</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-fbd0b81d9668781074ebd4c6972e91abb6afbc976392e2896795e43fdf20fc5e3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2013</creationdate><topic>Equations</topic><topic>Extended Kalman Filter</topic><topic>Kalman filters</topic><topic>Mathematical model</topic><topic>Phasor measurement units</topic><topic>Power system dynamics</topic><topic>Power system stability</topic><topic>Power system state estimation</topic><topic>State estimation</topic><topic>Unscented Kalman Filter</topic><toplevel>online_resources</toplevel><creatorcontrib>Tebianian, Hamed</creatorcontrib><creatorcontrib>Jeyasurya, Benjamin</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Tebianian, Hamed</au><au>Jeyasurya, Benjamin</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Dynamic state estimation in power systems using Kalman filters</atitle><btitle>2013 IEEE Electrical Power & Energy Conference</btitle><stitle>EPEC</stitle><date>2013-08</date><risdate>2013</risdate><spage>1</spage><epage>5</epage><pages>1-5</pages><eisbn>9781479901067</eisbn><eisbn>1479901067</eisbn><eisbn>9781479901050</eisbn><eisbn>1479901059</eisbn><abstract>Dynamic state estimation of power systems is necessary for wide area control purposes. Among the states of synchronous machine, precise, accurate, and timely information about rotor angle and speed deviation can be useful to enhance power system reliability and stability. In this paper two popular nonlinear estimation approaches: Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) are used to estimate main states of a simple power system using high rate data provided by Phasor Measurement Unit (PMU). A case study using a simple power system model is presented to illustrate the effectiveness of proposed approaches.</abstract><pub>IEEE</pub><doi>10.1109/EPEC.2013.6802979</doi><tpages>5</tpages></addata></record> |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Equations Extended Kalman Filter Kalman filters Mathematical model Phasor measurement units Power system dynamics Power system stability Power system state estimation State estimation Unscented Kalman Filter |
| title | Dynamic state estimation in power systems using Kalman filters |
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