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Article: Delay Aware Power System Synchrophasor Recovery and Prediction Framework
Title | Delay Aware Power System Synchrophasor Recovery and Prediction Framework |
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Authors | |
Keywords | communication latency data recovery deep learning synchrophasor Wide-area measurement system |
Issue Date | 2019 |
Publisher | Institute 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? |
Abstract | This 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 Identifier | http://hdl.handle.net/10722/275011 |
ISSN | 2023 Impact Factor: 8.6 2023 SCImago Journal Rankings: 4.863 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Yu, JJ | - |
dc.contributor.author | Lam, AYS | - |
dc.contributor.author | Hill, DJ | - |
dc.contributor.author | Hou, Y | - |
dc.contributor.author | Li, VOK | - |
dc.date.accessioned | 2019-09-10T02:33:37Z | - |
dc.date.available | 2019-09-10T02:33:37Z | - |
dc.date.issued | 2019 | - |
dc.identifier.citation | IEEE Transactions on Smart Grid, 2019, v. 10 n. 4, p. 3732-3742 | - |
dc.identifier.issn | 1949-3053 | - |
dc.identifier.uri | http://hdl.handle.net/10722/275011 | - |
dc.description.abstract | This 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.language | eng | - |
dc.publisher | Institute of Electrical and Electronics Engineers. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=5165411 | - |
dc.relation.ispartof | IEEE Transactions on Smart Grid | - |
dc.rights | IEEE 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.subject | communication latency | - |
dc.subject | data recovery | - |
dc.subject | deep learning | - |
dc.subject | synchrophasor | - |
dc.subject | Wide-area measurement system | - |
dc.title | Delay Aware Power System Synchrophasor Recovery and Prediction Framework | - |
dc.type | Article | - |
dc.identifier.email | Yu, JJ: jqyu@eee.hku.hk | - |
dc.identifier.email | Lam, AYS: ayslam@eee.hku.hk | - |
dc.identifier.email | Hill, DJ: dhill@eee.hku.hk | - |
dc.identifier.email | Hou, Y: yhhou@hku.hk | - |
dc.identifier.email | Li, VOK: vli@eee.hku.hk | - |
dc.identifier.authority | Lam, AYS=rp02083 | - |
dc.identifier.authority | Hill, DJ=rp01669 | - |
dc.identifier.authority | Hou, Y=rp00069 | - |
dc.identifier.authority | Li, VOK=rp00150 | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/TSG.2018.2834543 | - |
dc.identifier.scopus | eid_2-s2.0-85046767323 | - |
dc.identifier.hkuros | 302917 | - |
dc.identifier.volume | 10 | - |
dc.identifier.issue | 4 | - |
dc.identifier.spage | 3732 | - |
dc.identifier.epage | 3742 | - |
dc.identifier.isi | WOS:000472577500021 | - |
dc.publisher.place | United States | - |
dc.identifier.issnl | 1949-3053 | - |