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Article: Intelligent Time-Adaptive Transient Stability Assessment System

TitleIntelligent Time-Adaptive Transient Stability Assessment System
Authors
KeywordsLong short-term memory
Phasor measurement units
Recurrent neural network
Transient stability assessment
Voltage phasor.
Issue Date2017
PublisherInstitute of Electrical and Electronics Engineers. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=59
Citation
IEEE Transactions on Power Systems, 2017, v. 33 n. 1, p. 1049-1058 How to Cite?
AbstractOnline identification of post-contingency transient stability is essential in power system control, as it facilitates the grid operator to decide and coordinate system failure correction control actions. Utilizing machine learning methods with synchrophasor measurements for transient stability assessment has received much attention recently with the gradual deployment of wide-area protection and control systems. In this paper, we develop a transient stability assessment system based on the long short-term memory network. By proposing a temporal self-adaptive scheme, our proposed system aims to balance the trade-off between assessment accuracy and response time, both of which may be crucial in real-world scenarios. Compared with previous work, the most significant enhancement is that our system learns from the temporal data dependencies of the input data, which contributes to better assessment accuracy. In addition, the model structure of our system is relatively less complex, speeding up the model training process. Case studies on three power systems demonstrate the efficacy of the proposed transient stability assessment system.
Persistent Identifierhttp://hdl.handle.net/10722/246246
ISSN
2017 Impact Factor: 5.255
2015 SCImago Journal Rankings: 4.126
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorYu, JJ-
dc.contributor.authorHill, DJ-
dc.contributor.authorLam, AYS-
dc.contributor.authorGu, J-
dc.contributor.authorLi, VOK-
dc.date.accessioned2017-09-18T02:25:06Z-
dc.date.available2017-09-18T02:25:06Z-
dc.date.issued2017-
dc.identifier.citationIEEE Transactions on Power Systems, 2017, v. 33 n. 1, p. 1049-1058-
dc.identifier.issn0885-8950-
dc.identifier.urihttp://hdl.handle.net/10722/246246-
dc.description.abstractOnline identification of post-contingency transient stability is essential in power system control, as it facilitates the grid operator to decide and coordinate system failure correction control actions. Utilizing machine learning methods with synchrophasor measurements for transient stability assessment has received much attention recently with the gradual deployment of wide-area protection and control systems. In this paper, we develop a transient stability assessment system based on the long short-term memory network. By proposing a temporal self-adaptive scheme, our proposed system aims to balance the trade-off between assessment accuracy and response time, both of which may be crucial in real-world scenarios. Compared with previous work, the most significant enhancement is that our system learns from the temporal data dependencies of the input data, which contributes to better assessment accuracy. In addition, the model structure of our system is relatively less complex, speeding up the model training process. Case studies on three power systems demonstrate the efficacy of the proposed transient stability assessment system.-
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=59-
dc.relation.ispartofIEEE Transactions on Power Systems-
dc.rightsIEEE Transactions on Power Systems. 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.subjectLong short-term memory-
dc.subjectPhasor measurement units-
dc.subjectRecurrent neural network-
dc.subjectTransient stability assessment-
dc.subjectVoltage phasor.-
dc.titleIntelligent Time-Adaptive Transient Stability Assessment System-
dc.typeArticle-
dc.identifier.emailYu, JJ: jqyu@eee.hku.hk-
dc.identifier.emailHill, DJ: dhill@eee.hku.hk-
dc.identifier.emailLam, AYS: ayslam@eee.hku.hk-
dc.identifier.emailLi, VOK: vli@eee.hku.hk-
dc.identifier.authorityHill, DJ=rp01669-
dc.identifier.authorityLam, AYS=rp02083-
dc.identifier.authorityLi, VOK=rp00150-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/TPWRS.2017.2707501-
dc.identifier.scopuseid_2-s2.0-85045247917-
dc.identifier.hkuros277339-
dc.identifier.volume33-
dc.identifier.issue1-
dc.identifier.spage1049-
dc.identifier.epage1058-
dc.identifier.isiWOS:000418776400093-
dc.publisher.placeUnited States-

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