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- Publisher Website: 10.1109/TPWRS.2019.2957377
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Article: Hierarchical Deep Learning Machine for Power System Online Transient Stability Prediction
Title | Hierarchical Deep Learning Machine for Power System Online Transient Stability Prediction |
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Authors | |
Keywords | Transient analysis Power system stability Stability analysis Trajectory Thermal stability |
Issue Date | 2020 |
Publisher | Institute 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, 2020, v. 35 n. 3, p. 2399-2411 How to Cite? |
Abstract | This paper develops a hierarchical deep learning machine (HDLM) to efficiently achieve both quantitative and qualitative online transient stability prediction (TSP). For the sake of improving its online efficiency, multiple generators' fault-on trajectories as well as the two closest data-points in pre-/post-fault stages are acquired by PMUs to form its raw inputs. An anti-noise graphical transient characterization technique is tactfully designed to transform multiplex trajectories into 2-D images, within which system-wide transients are concisely described. Then, following the divide-and-conquer philosophy, the HDLM trains a two-level convolutional neural network (CNN) based regression model. With stability margin regressions hierarchically refined, it manages to perform reliable and adaptive online TSP almost immediately after fault clearance. Test results on the IEEE 39-bus test system and the real-world Guangdong Power Grid in South China demonstrate the HDLM's superior performances on both stability status and stability margin predictions. |
Persistent Identifier | http://hdl.handle.net/10722/287652 |
ISSN | 2023 Impact Factor: 6.5 2023 SCImago Journal Rankings: 3.827 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Zhu, L | - |
dc.contributor.author | Hill, DJ | - |
dc.contributor.author | Lu, C | - |
dc.date.accessioned | 2020-10-05T12:01:15Z | - |
dc.date.available | 2020-10-05T12:01:15Z | - |
dc.date.issued | 2020 | - |
dc.identifier.citation | IEEE Transactions on Power Systems, 2020, v. 35 n. 3, p. 2399-2411 | - |
dc.identifier.issn | 0885-8950 | - |
dc.identifier.uri | http://hdl.handle.net/10722/287652 | - |
dc.description.abstract | This paper develops a hierarchical deep learning machine (HDLM) to efficiently achieve both quantitative and qualitative online transient stability prediction (TSP). For the sake of improving its online efficiency, multiple generators' fault-on trajectories as well as the two closest data-points in pre-/post-fault stages are acquired by PMUs to form its raw inputs. An anti-noise graphical transient characterization technique is tactfully designed to transform multiplex trajectories into 2-D images, within which system-wide transients are concisely described. Then, following the divide-and-conquer philosophy, the HDLM trains a two-level convolutional neural network (CNN) based regression model. With stability margin regressions hierarchically refined, it manages to perform reliable and adaptive online TSP almost immediately after fault clearance. Test results on the IEEE 39-bus test system and the real-world Guangdong Power Grid in South China demonstrate the HDLM's superior performances on both stability status and stability margin predictions. | - |
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=59 | - |
dc.relation.ispartof | IEEE Transactions on Power Systems | - |
dc.rights | IEEE 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.subject | Transient analysis | - |
dc.subject | Power system stability | - |
dc.subject | Stability analysis | - |
dc.subject | Trajectory | - |
dc.subject | Thermal stability | - |
dc.title | Hierarchical Deep Learning Machine for Power System Online Transient Stability Prediction | - |
dc.type | Article | - |
dc.identifier.email | Zhu, L: zhulp@hku.hk | - |
dc.identifier.email | Hill, DJ: dhill@eee.hku.hk | - |
dc.identifier.authority | Hill, DJ=rp01669 | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/TPWRS.2019.2957377 | - |
dc.identifier.scopus | eid_2-s2.0-85083832362 | - |
dc.identifier.hkuros | 315107 | - |
dc.identifier.volume | 35 | - |
dc.identifier.issue | 3 | - |
dc.identifier.spage | 2399 | - |
dc.identifier.epage | 2411 | - |
dc.identifier.isi | WOS:000529523600061 | - |
dc.publisher.place | United States | - |
dc.identifier.issnl | 0885-8950 | - |