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Article: Delay Aware Power System Synchrophasor Recovery and Prediction Framework

TitleDelay Aware Power System Synchrophasor Recovery and Prediction Framework
Authors
Keywordscommunication latency
data recovery
deep learning
synchrophasor
Wide-area measurement system
Issue Date2019
PublisherInstitute of Electrical and Electronics Engineers. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=5165411
Citation
IEEE Transactions on Smart Grid, 2019, v. 10 n. 4, p. 3732-3742 How to Cite?
AbstractThis paper presents a novel delay aware synchrophasor recovery and prediction framework to address the problem of missing power system state variables due to the existence of communication latency. This capability is particularly essential for dynamic power system scenarios where fast remedial control actions are required due to system events or faults. While a wide area measurement system can sample high-frequency system states with phasor measurement units, the control center cannot obtain them in real-time due to latency and data loss. In this work, a synchrophasor recovery and prediction framework and its practical implementation are proposed to recover the current system state and predict future states utilizing existing incomplete synchrophasor data. The framework establishes an iterative prediction scheme, and the proposed implementation adopts recent machine learning advances in data processing. Simulation results indicate the superior accuracy and speed of the proposed framework, and investigations are made to study its sensitivity to various communication delay patterns for pragmatic applications. © 2010-2012 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/275011
ISSN
2023 Impact Factor: 8.6
2023 SCImago Journal Rankings: 4.863
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorYu, JJ-
dc.contributor.authorLam, AYS-
dc.contributor.authorHill, DJ-
dc.contributor.authorHou, Y-
dc.contributor.authorLi, VOK-
dc.date.accessioned2019-09-10T02:33:37Z-
dc.date.available2019-09-10T02:33:37Z-
dc.date.issued2019-
dc.identifier.citationIEEE Transactions on Smart Grid, 2019, v. 10 n. 4, p. 3732-3742-
dc.identifier.issn1949-3053-
dc.identifier.urihttp://hdl.handle.net/10722/275011-
dc.description.abstractThis paper presents a novel delay aware synchrophasor recovery and prediction framework to address the problem of missing power system state variables due to the existence of communication latency. This capability is particularly essential for dynamic power system scenarios where fast remedial control actions are required due to system events or faults. While a wide area measurement system can sample high-frequency system states with phasor measurement units, the control center cannot obtain them in real-time due to latency and data loss. In this work, a synchrophasor recovery and prediction framework and its practical implementation are proposed to recover the current system state and predict future states utilizing existing incomplete synchrophasor data. The framework establishes an iterative prediction scheme, and the proposed implementation adopts recent machine learning advances in data processing. Simulation results indicate the superior accuracy and speed of the proposed framework, and investigations are made to study its sensitivity to various communication delay patterns for pragmatic applications. © 2010-2012 IEEE.-
dc.languageeng-
dc.publisherInstitute of Electrical and Electronics Engineers. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=5165411-
dc.relation.ispartofIEEE Transactions on Smart Grid-
dc.rightsIEEE Transactions on Smart Grid. Copyright © Institute of Electrical and Electronics Engineers.-
dc.rights©20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.-
dc.subjectcommunication latency-
dc.subjectdata recovery-
dc.subjectdeep learning-
dc.subjectsynchrophasor-
dc.subjectWide-area measurement system-
dc.titleDelay Aware Power System Synchrophasor Recovery and Prediction Framework-
dc.typeArticle-
dc.identifier.emailYu, JJ: jqyu@eee.hku.hk-
dc.identifier.emailLam, AYS: ayslam@eee.hku.hk-
dc.identifier.emailHill, DJ: dhill@eee.hku.hk-
dc.identifier.emailHou, Y: yhhou@hku.hk-
dc.identifier.emailLi, VOK: vli@eee.hku.hk-
dc.identifier.authorityLam, AYS=rp02083-
dc.identifier.authorityHill, DJ=rp01669-
dc.identifier.authorityHou, Y=rp00069-
dc.identifier.authorityLi, VOK=rp00150-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/TSG.2018.2834543-
dc.identifier.scopuseid_2-s2.0-85046767323-
dc.identifier.hkuros302917-
dc.identifier.volume10-
dc.identifier.issue4-
dc.identifier.spage3732-
dc.identifier.epage3742-
dc.identifier.isiWOS:000472577500021-
dc.publisher.placeUnited States-
dc.identifier.issnl1949-3053-

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