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dc.contributor.authorRojas Dueñas, Gabriel
dc.contributor.authorRiba Ruiz, Jordi-Roger
dc.contributor.authorMoreno Eguilaz, Juan Manuel
dc.contributor.authorKadechkar, Akash
dc.contributor.authorGómez Pau, Álvaro
dc.contributor.otherUniversitat Politècnica de Catalunya. Doctorat en Enginyeria Elèctrica
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Enginyeria Elèctrica
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Enginyeria Electrònica
dc.date.accessioned2020-05-19T12:38:50Z
dc.date.issued2020
dc.identifier.citationRojas, G. [et al.]. Black-box modelling of a DC-DC buck converter based on a recurrent neural network. A: IEEE International Conference on Industrial Technology. "Proceeding IEEE International Conference on Industrial Technology (ICIT) 2020". Institute of Electrical and Electronics Engineers (IEEE), 2020, p. 456-461.
dc.identifier.urihttp://hdl.handle.net/2117/188104
dc.description.abstractNeural network; Power converter; Training; Prediction; System identification; Black-box model
dc.format.extent6 p.
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.subjectÀrees temàtiques de la UPC::Enginyeria electrònica::Electrònica de potència::Convertidors de corrent elèctric
dc.subject.lcshDC-to-DC converters
dc.subject.otherArtificial neural networks allow the identification of black-box models. This paper proposes a method aimed at replicating the static and dynamic behavior of a DC-DC power converter based on a recurrent nonlinear autoregressive exogenous neural network. The method proposed in this work applies an algorithm that trains a neural network based on the inputs and outputs (currents and voltages) of a Buck converter. The approach is validated by means of simulated data of a realistic nonsynchronous Buck converter model programmed in Simulink and by means of experimental results. The predictions made by the neural network are compared to the actual outputs of the system
dc.subject.otherto determine the accuracy of the method
dc.subject.otherthus validating the proposed approach. Both simulation and experimental results show the feasibility and accuracy of the proposed black-box approach.
dc.titleBlack-box modelling of a DC-DC buck converter based on a recurrent neural network
dc.typeConference lecture
dc.subject.lemacConvertidors continu-continu
dc.contributor.groupUniversitat Politècnica de Catalunya. MCIA - Motion Control and Industrial Applications Research Group
dc.contributor.groupUniversitat Politècnica de Catalunya. QINE - Disseny de Baix Consum, Test, Verificació i Circuits Integrats de Seguretat
dc.identifier.doi10.1109/ICIT45562.2020.9067098
dc.relation.publisherversionhttps://ieeexplore.ieee.org/document/9067098
dc.rights.accessOpen Access
local.identifier.drac27790878
dc.description.versionPostprint (author's final draft)
dc.date.lift10000-01-01
local.citation.authorRojas, G.; Riba, J.; Moreno-Eguilaz, J.M.; Kadechkar, A.; Álvaro Gómez-Pau
local.citation.contributorIEEE International Conference on Industrial Technology
local.citation.publicationNameProceeding IEEE International Conference on Industrial Technology (ICIT) 2020
local.citation.startingPage456
local.citation.endingPage461


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