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State Estimation of Doubly Fed Induction Generator Wind Turbine in Complex Power Systems
This paper presents a general framework for the doubly fed induction generator connected to a complex power system in order to facilitate the dynamic estimation of its states using noisy PMU measurements. State estimation considering the whole power system with the occurrence of electric faults is p...
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Published in: | IEEE transactions on power systems 2016-11, Vol.31 (6), p.4935-4944 |
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container_title | IEEE transactions on power systems |
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creator | Shenglong Yu Emami, Kianoush Fernando, Tyrone Iu, Herbert H. C. Kit Po Wong |
description | This paper presents a general framework for the doubly fed induction generator connected to a complex power system in order to facilitate the dynamic estimation of its states using noisy PMU measurements. State estimation considering the whole power system with the occurrence of electric faults is performed using the Unscented Kalman Filter (UKF) with a bad data detection scheme. Such a state estimation scheme for a DFIG is important because not all dynamic states of a DFIG are easily measurable. Furthermore, the proposed state estimation technique is decentralized and the network topology of the entire power system is taken into consideration in the estimation process. In order to enhance the error tolerance and self-correction of the power system, bad data detection technique is implemented. A performance comparison with Extended Kalman Filter (EKF) is also discussed. |
doi_str_mv | 10.1109/TPWRS.2015.2507620 |
format | article |
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(IEEE) 2016</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c328t-2d2e8835887013934e376616cdfe4c9c042cf7ad073f42570079e9799172b76f3</citedby><cites>FETCH-LOGICAL-c328t-2d2e8835887013934e376616cdfe4c9c042cf7ad073f42570079e9799172b76f3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/7384773$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,776,780,27903,27904,54775</link.rule.ids></links><search><creatorcontrib>Shenglong Yu</creatorcontrib><creatorcontrib>Emami, Kianoush</creatorcontrib><creatorcontrib>Fernando, Tyrone</creatorcontrib><creatorcontrib>Iu, Herbert H. 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In order to enhance the error tolerance and self-correction of the power system, bad data detection technique is implemented. A performance comparison with Extended Kalman Filter (EKF) is also discussed.</description><subject>Bad data detection</subject><subject>doubly fed induction generator (DFIG)</subject><subject>Dynamical systems</subject><subject>Dynamics</subject><subject>EKF</subject><subject>Electric power systems</subject><subject>Error detection</subject><subject>Induction generators</subject><subject>Mathematical model</subject><subject>PMU</subject><subject>Power supply</subject><subject>Power system dynamics</subject><subject>Rotors</subject><subject>State estimation</subject><subject>Stators</subject><subject>Tolerances</subject><subject>UKF</subject><subject>Wind turbines</subject><issn>0885-8950</issn><issn>1558-0679</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><recordid>eNpdkE1Lw0AQhhdRsFb_gF4WvHhJnd3NZnePUttaECy2Um8h3UwgJc3W3QTtvzf9wIOngXeedxgeQm4ZDBgD87iYLd_nAw5MDrgElXA4Iz0mpY4gUeac9EBrGWkj4ZJchbAGgKRb9MjnvMkapKPQlJusKV1NXUGfXbuqdnSMOZ3WeWsP-QRr9FnjPF2WdU4XrV-VNdKypkO32Vb4Q2fuGz2d70KDm3BNLoqsCnhzmn3yMR4thi_R69tkOnx6jazguol4zlFrIbVWwIQRMQqVJCyxeYGxNRZibguV5aBEEXOpAJRBo4xhiq9UUog-eTje3Xr31WJo0k0ZLFZVVqNrQ8q0lEJJyUyH3v9D1671dfddRwnGDTMm7ih-pKx3IXgs0q3v3PhdyiDdy04PstO97PQkuyvdHUslIv4VlNCxUkL8At5heY0</recordid><startdate>201611</startdate><enddate>201611</enddate><creator>Shenglong Yu</creator><creator>Emami, Kianoush</creator><creator>Fernando, Tyrone</creator><creator>Iu, Herbert H. 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C.</au><au>Kit Po Wong</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>State Estimation of Doubly Fed Induction Generator Wind Turbine in Complex Power Systems</atitle><jtitle>IEEE transactions on power systems</jtitle><stitle>TPWRS</stitle><date>2016-11</date><risdate>2016</risdate><volume>31</volume><issue>6</issue><spage>4935</spage><epage>4944</epage><pages>4935-4944</pages><issn>0885-8950</issn><eissn>1558-0679</eissn><coden>ITPSEG</coden><abstract>This paper presents a general framework for the doubly fed induction generator connected to a complex power system in order to facilitate the dynamic estimation of its states using noisy PMU measurements. State estimation considering the whole power system with the occurrence of electric faults is performed using the Unscented Kalman Filter (UKF) with a bad data detection scheme. Such a state estimation scheme for a DFIG is important because not all dynamic states of a DFIG are easily measurable. Furthermore, the proposed state estimation technique is decentralized and the network topology of the entire power system is taken into consideration in the estimation process. In order to enhance the error tolerance and self-correction of the power system, bad data detection technique is implemented. A performance comparison with Extended Kalman Filter (EKF) is also discussed.</abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/TPWRS.2015.2507620</doi><tpages>10</tpages></addata></record> |
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source | IEEE Electronic Library (IEL) Journals |
subjects | Bad data detection doubly fed induction generator (DFIG) Dynamical systems Dynamics EKF Electric power systems Error detection Induction generators Mathematical model PMU Power supply Power system dynamics Rotors State estimation Stators Tolerances UKF Wind turbines |
title | State Estimation of Doubly Fed Induction Generator Wind Turbine in Complex Power Systems |
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