Modeling and simulation of position estimation of switched reluctance motor with artificial neural networks

Basit Öğe Kaydı
dc.authorid0000-0002-1821-0722
dc.contributor.authorÜstün, Oğuz
dc.contributor.authorBekiroğlu, Erdal
dc.date.accessioned2021-06-23T18:52:06Z
dc.date.available2021-06-23T18:52:06Z
dc.date.issued2009
dc.departmentBAİBÜ, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümüen_US
dc.description.abstractIn the present study, position estimation of switched reluctance motor (SRM) has been achieved on the basis of the artificial neural networks (ANNs). The ANNs can estimate the rotor position without using an extra rotor position sensor by measuring the phase flux linkages and phase currents. Flux linkage-phase current-rotor position data set and supervised back propagation learning algorithm are used in training of the ANN based position estimator. A 4-phase SRM have been used to verify the accuracy and feasibility of the proposed position estimator. Simulation results show that the proposed position estimator gives precise and accurate position estimations for both under the low and high level reference speeds of the SRM.en_US
dc.identifier.endpage34en_US
dc.identifier.issn2010-376X
dc.identifier.scopus2-s2.0-78651587186en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage30en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12491/4131
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-78651587186&partnerID=40&md5=e6bea610af10d13f402a756aa1e9a26c
dc.identifier.volume57en_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorÜstün, Oğuz
dc.institutionauthorBekiroğlu, Erdal
dc.language.isoenen_US
dc.relation.ispartofWorld Academy of Science, Engineering and Technologyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectArtificial Neural Networksen_US
dc.subjectModeling And Simulationen_US
dc.subjectPosition Observeren_US
dc.subjectSwitched Reluctance Motoren_US
dc.titleModeling and simulation of position estimation of switched reluctance motor with artificial neural networksen_US
dc.typeArticleen_US

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