The Speed Estimation via BiLSTM-Based Network of a BLDC Motor Drive for Fan Applications

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dc.contributor.authorUnlersen, Muhammed Fahri
dc.contributor.authorBalci, Selami
dc.contributor.authorAslan, Muhammet Fatih
dc.contributor.authorSabanci, Kadir
dc.date.accessioned2024-02-23T14:00:04Z
dc.date.available2024-02-23T14:00:04Z
dc.date.issued2022
dc.departmentNEÜen_US
dc.description.abstractIn this study, in order to determine the dynamic response of a four-pole permanent magnet three-phase brushless DC (BLDC) motor, parametric simulation studies are carried out with finite element analysis Rmxprt software depending on three specific input variables (excitation voltage, pulse width, and motor power). The rotor speed is defined as the output parameter to determine the dynamic response, and 600 parametric data are obtained according to the simulation studies. In order to estimate the rotor speed of the BLDC motor modeled using artificial intelligence (AI), an advanced recurrent neural network architecture known as bidirectional long short-term memory has been designed. Rotor speed is successfully estimated with the proposed architecture, and as a result, the mean absolute percentage error value is calculated as 3.25%. These results show that the analysis of BLDC motor parameters can be determined quickly with the proposed AI method without long-running simulations.en_US
dc.identifier.doi10.1007/s13369-021-05700-w
dc.identifier.endpage2648en_US
dc.identifier.issn2193-567X
dc.identifier.issn2191-4281
dc.identifier.issue3en_US
dc.identifier.scopus2-s2.0-85106024415en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage2639en_US
dc.identifier.urihttps://doi.org/10.1007/s13369-021-05700-w
dc.identifier.urihttps://hdl.handle.net/20.500.12452/11445
dc.identifier.volume47en_US
dc.identifier.wosWOS:000652425100001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringer Heidelbergen_US
dc.relation.ispartofArabian Journal For Science And Engineeringen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectBldc Motoren_US
dc.subjectLong Short-Term Memoryen_US
dc.subjectRecurrent Neural Networken_US
dc.subjectRotor Speeden_US
dc.subjectParameter Estimationen_US
dc.titleThe Speed Estimation via BiLSTM-Based Network of a BLDC Motor Drive for Fan Applicationsen_US
dc.typeArticleen_US

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