Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/146694
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dc.contributor.authorTun, Pyae Phyoen_US
dc.contributor.authorKumar, Padmanabhan Sampathen_US
dc.contributor.authorPratama, Ryan Aryaen_US
dc.contributor.authorLiu, Shuyongen_US
dc.date.accessioned2021-03-05T04:58:42Z-
dc.date.available2021-03-05T04:58:42Z-
dc.date.issued2019-
dc.identifier.citationTun, P. P., Kumar, P. S., Pratama, R. A., Liu, S. (2019). Brushless synchronous generator turn-to-turn short circuit fault detection using multilayer neural network. Proceeding of 2018 Asian Conference on Energy, Power and Transportation Electrification (ACEPT). doi:10.1109/ACEPT.2018.8610686en_US
dc.identifier.isbn9781538681367-
dc.identifier.urihttps://hdl.handle.net/10356/146694-
dc.description.abstractStator winding short circuit is one of the faults that occur frequently in electrical machines. Therefore, fault detection and elimination in electric drive systems is necessary for safety-critical applications in order not to cause catastrophic failure to the machine in a short time. This paper reviews recent fault detection and diagnosis techniques that use signal analysis, model-based techniques and artificial intelligence machine diagnosis methods. Then, feedforward neural network will be trained, tested and validated whether or not this artificial neural network can classified healthy and different severity inter-turn short circuit levels by using per unit RMS 3 phases current and voltage quantities as well as fundamental and third harmonic components of current and voltage.en_US
dc.language.isoenen_US
dc.rights© 2018 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. The published version is available at: https://doi.org/10.1109/ACEPT.2018.8610686.en_US
dc.subjectEngineering::Electrical and electronic engineeringen_US
dc.titleBrushless synchronous generator turn-to-turn short circuit fault detection using multilayer neural networken_US
dc.typeConference Paperen
dc.contributor.conference2018 Asian Conference on Energy, Power and Transportation Electrification (ACEPT)en_US
dc.contributor.organizationRolls-royce Singapore Pte Ltden_US
dc.contributor.researchEnergy Research Institute @ NTU (ERI@N)en_US
dc.contributor.researchRolls-Royce@NTU Corporate Laben_US
dc.identifier.doi10.1109/ACEPT.2018.8610686-
dc.description.versionAccepted versionen_US
dc.identifier.scopus2-s2.0-85062075659-
dc.subject.keywordsBrushless Synchronous Generatoren_US
dc.subject.keywordsPower Generationen_US
dc.citation.conferencelocationSingaporeen_US
item.grantfulltextopen-
item.fulltextWith Fulltext-
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