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Black-box modelling of a DC-DC buck converter based on a recurrent neural network
dc.contributor.author | Rojas Dueñas, Gabriel |
dc.contributor.author | Riba Ruiz, Jordi-Roger |
dc.contributor.author | Moreno Eguilaz, Juan Manuel |
dc.contributor.author | Kadechkar, Akash |
dc.contributor.author | Gómez Pau, Álvaro |
dc.contributor.other | Universitat Politècnica de Catalunya. Doctorat en Enginyeria Elèctrica |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament d'Enginyeria Elèctrica |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament d'Enginyeria Electrònica |
dc.date.accessioned | 2020-05-19T12:38:50Z |
dc.date.issued | 2020 |
dc.identifier.citation | Rojas, 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.uri | http://hdl.handle.net/2117/188104 |
dc.description.abstract | Neural network; Power converter; Training; Prediction; System identification; Black-box model |
dc.format.extent | 6 p. |
dc.language.iso | eng |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) |
dc.subject | Àrees temàtiques de la UPC::Enginyeria electrònica::Electrònica de potència::Convertidors de corrent elèctric |
dc.subject.lcsh | DC-to-DC converters |
dc.subject.other | Artificial 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.other | to determine the accuracy of the method |
dc.subject.other | thus validating the proposed approach. Both simulation and experimental results show the feasibility and accuracy of the proposed black-box approach. |
dc.title | Black-box modelling of a DC-DC buck converter based on a recurrent neural network |
dc.type | Conference lecture |
dc.subject.lemac | Convertidors continu-continu |
dc.contributor.group | Universitat Politècnica de Catalunya. MCIA - Motion Control and Industrial Applications Research Group |
dc.contributor.group | Universitat Politècnica de Catalunya. QINE - Disseny de Baix Consum, Test, Verificació i Circuits Integrats de Seguretat |
dc.identifier.doi | 10.1109/ICIT45562.2020.9067098 |
dc.relation.publisherversion | https://ieeexplore.ieee.org/document/9067098 |
dc.rights.access | Open Access |
local.identifier.drac | 27790878 |
dc.description.version | Postprint (author's final draft) |
dc.date.lift | 10000-01-01 |
local.citation.author | Rojas, G.; Riba, J.; Moreno-Eguilaz, J.M.; Kadechkar, A.; Álvaro Gómez-Pau |
local.citation.contributor | IEEE International Conference on Industrial Technology |
local.citation.publicationName | Proceeding IEEE International Conference on Industrial Technology (ICIT) 2020 |
local.citation.startingPage | 456 |
local.citation.endingPage | 461 |