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dc.contributor.authorLiao, H
dc.contributor.authorAnani, N
dc.date.accessioned2020-08-27T09:53:48Z
dc.date.available2020-08-27T09:53:48Z
dc.date.issued2017-10-23
dc.identifier.citationLiao, H. and Anani, N. (2017) Fault identification-based voltage sag state estimation using artificial neural network. Energy Procedia, 134, 40-47.en
dc.identifier.issn1876-6102en
dc.identifier.doi10.1016/j.egypro.2017.09.596en
dc.identifier.urihttp://hdl.handle.net/2436/623526
dc.description© 2017 The Authors. Published by Elsevier. This is an open access article available under a Creative Commons licence. The published version can be accessed at the following link on the publisher’s website: https://doi.org/10.1016/j.egypro.2017.09.596en
dc.description.abstract© 2017 The Authors. Published by Elsevier Ltd. This paper presents an artificial neural network (ANN) based approach to identify faults for voltage sag state estimation. Usually ANN cannot be used to abstract relationship between monitored data and arbitrarily named fault indices which are not related at all logically in numerical level. This paper presents a novel approach to overcome this problem. In this approach, not only the networks are trained to adapt to the given training data, the training data (the expected outputs of fault indices) is also updated to adapt to the neural network. During the training procedure, both the neural networks and training data are updated interactively. With the proposed approach, various faults can be accurately identified using limited monitored data. The approach is robust to measurement uncertainty which usually exists in practical monitoring systems. Furthermore, the updated fault indices are able to suggest the difference of the impact of various faults on bus voltages. .en
dc.formatapplication/pdfen
dc.languageen
dc.language.isoenen
dc.publisherElsevieren
dc.relation.urlhttps://www.sciencedirect.com/science/article/pii/S1876610217347306?via%3Dihuben
dc.subjectVoltage sag state estimationen
dc.subjectartificial neural networken
dc.subjectfault indicesen
dc.subjectpower qualityen
dc.subjectstate estimationen
dc.titleFault Identification-based voltage sag state estimation using artificial neural networken
dc.typeJournal articleen
dc.identifier.journalEnergy Procediaen
dc.date.updated2020-08-18T20:30:29Z
rioxxterms.funderSheffield Hallam Universityen
rioxxterms.identifier.projectUOW27082020NAen
rioxxterms.versionVoRen
rioxxterms.licenseref.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/en
rioxxterms.licenseref.startdate2020-08-27en
dc.source.volume134
dc.source.beginpage40
dc.source.endpage47
dc.description.versionPublished version
refterms.dateFCD2020-08-27T09:53:36Z
refterms.versionFCDVoR
refterms.dateFOA2020-08-27T09:53:48Z


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