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- Publisher Website: 10.1109/TIE.2019.2917367
- Scopus: eid_2-s2.0-85067008697
- WOS: WOS:000516608400078
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Article: Distributed Fast Fault Diagnosis for Multimachine Power Systems via Deterministic Learning
Title | Distributed Fast Fault Diagnosis for Multimachine Power Systems via Deterministic Learning |
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Authors | |
Keywords | Deterministic learning (DL) distributed fault diagnosis multimachine power systems persistent excitation (PE) condition radial basis function (RBF) neural networks (NNs) |
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=41 |
Citation | IEEE Transactions on Industrial Electronics, 2020, v. 67 n. 5, p. 4152 -4162 How to Cite? |
Abstract | In this paper, a distributed fast fault diagnosis approach is proposed for multimachine power systems based on deterministic learning (DL) theory. First, a learning estimator is constructed to accumulate the knowledge of transient fault dynamics in power systems. Through DL, a partial persistent excitation condition of the local neurons is satisfied and convergence of the neural weights is achieved. Thus, a knowledge bank can be established and gradually updated. To deal with the high dimensional data, only the neurons centered in a neighborhood of the system trajectory are activated. In this manner, the computation load is mitigated. Second, by using the learnt knowledge, a distributed fault diagnosis scheme is designed to monitor the power system. The memory of the transient fault dynamics can be quickly recalled and a fast fault diagnosis decision is made. Finally, based on the concepts of mismatch interval and duty ratio, the diagnosis capabilities of the proposed scheme are investigated. The effectiveness of the proposed diagnosis scheme is demonstrated by computer simulation and the hardware-in-loop experimental test based on the RT-LAB realtime simulator. |
Persistent Identifier | http://hdl.handle.net/10722/278961 |
ISSN | 2023 Impact Factor: 7.5 2023 SCImago Journal Rankings: 3.395 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Chen, T | - |
dc.contributor.author | Hill, DJ | - |
dc.contributor.author | Wang, C | - |
dc.date.accessioned | 2019-10-21T02:17:06Z | - |
dc.date.available | 2019-10-21T02:17:06Z | - |
dc.date.issued | 2020 | - |
dc.identifier.citation | IEEE Transactions on Industrial Electronics, 2020, v. 67 n. 5, p. 4152 -4162 | - |
dc.identifier.issn | 0278-0046 | - |
dc.identifier.uri | http://hdl.handle.net/10722/278961 | - |
dc.description.abstract | In this paper, a distributed fast fault diagnosis approach is proposed for multimachine power systems based on deterministic learning (DL) theory. First, a learning estimator is constructed to accumulate the knowledge of transient fault dynamics in power systems. Through DL, a partial persistent excitation condition of the local neurons is satisfied and convergence of the neural weights is achieved. Thus, a knowledge bank can be established and gradually updated. To deal with the high dimensional data, only the neurons centered in a neighborhood of the system trajectory are activated. In this manner, the computation load is mitigated. Second, by using the learnt knowledge, a distributed fault diagnosis scheme is designed to monitor the power system. The memory of the transient fault dynamics can be quickly recalled and a fast fault diagnosis decision is made. Finally, based on the concepts of mismatch interval and duty ratio, the diagnosis capabilities of the proposed scheme are investigated. The effectiveness of the proposed diagnosis scheme is demonstrated by computer simulation and the hardware-in-loop experimental test based on the RT-LAB realtime simulator. | - |
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=41 | - |
dc.relation.ispartof | IEEE Transactions on Industrial Electronics | - |
dc.rights | IEEE Transactions on Industrial Electronics. Copyright © Institute of Electrical and Electronics Engineers. | - |
dc.subject | Deterministic learning (DL) | - |
dc.subject | distributed fault diagnosis | - |
dc.subject | multimachine power systems | - |
dc.subject | persistent excitation (PE) condition | - |
dc.subject | radial basis function (RBF) neural networks (NNs) | - |
dc.title | Distributed Fast Fault Diagnosis for Multimachine Power Systems via Deterministic Learning | - |
dc.type | Article | - |
dc.identifier.email | Hill, DJ: dhill@eee.hku.hk | - |
dc.identifier.authority | Hill, DJ=rp01669 | - |
dc.identifier.doi | 10.1109/TIE.2019.2917367 | - |
dc.identifier.scopus | eid_2-s2.0-85067008697 | - |
dc.identifier.hkuros | 307232 | - |
dc.identifier.volume | 67 | - |
dc.identifier.issue | 5 | - |
dc.identifier.spage | 4152 | - |
dc.identifier.epage | 4162 | - |
dc.identifier.isi | WOS:000516608400078 | - |
dc.publisher.place | United States | - |
dc.identifier.issnl | 0278-0046 | - |