Denoising and Voltage Estimation in Modular Multilevel Converters Using Deep Neural-Networks

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dc.contributor.authorLangarica Chavira, Saúl Alberto
dc.contributor.authorPizarro Lorca, Germán Eduardo
dc.contributor.authorPoblete Durruty, Pablo Martín
dc.contributor.authorRadrigán Sepúlveda, Felipe Ignacio
dc.contributor.authorPereda Torres, Javier Eduardo
dc.contributor.authorRodriguez, Jose
dc.contributor.authorNúñez Retamal, Felipe Eduardo
dc.date.accessioned2022-05-18T14:39:48Z
dc.date.available2022-05-18T14:39:48Z
dc.date.issued2020
dc.description.abstractModular Multilevel Converters (MMCs) have become one of the most popular power converters for medium/high power applications, from transmission systems to motor drives. However, to operate properly, MMCs require a considerable number of sensors and communication of sensitive data to a central controller, all under relevant electromagnetic interference produced by the high frequency switching of power semiconductors. This work explores the use of neural networks (NNs) to support the operation of MMCs by: i) denoising measurements, such as stack currents, using a blind autoencoder NN; and ii) estimating the sub-module capacitor voltages, using an encoder-decoder NN. Experimental results obtained with data from a three-phase MMC show that NNs can effectively clean sensor measurements and estimate internal states of the converter accurately, even during transients, drastically reducing sensing and communication requirements.
dc.fechaingreso.objetodigital2024-05-29
dc.fuente.origenIEEE
dc.identifier.doi10.1109/ACCESS.2020.3038552
dc.identifier.issn2169-3536
dc.identifier.urihttps://doi.org/10.1109/ACCESS.2020.3038552
dc.identifier.urihttps://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9261401
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/64162
dc.information.autorucEscuela de ingeniería ; Langarica Chavira, Saúl Alberto ; S/I ; 222832
dc.information.autorucEscuela de ingeniería ; Pizarro Lorca, Germán Eduardo ; S/I ; 232465
dc.information.autorucEscuela de ingeniería ; Poblete Durruty, Pablo Martín ; S/I ; 232497
dc.information.autorucEscuela de ingeniería ; Radrigán Sepúlveda, Felipe Ignacio ; S/I ; 223382
dc.information.autorucEscuela de ingeniería ; Pereda Torres, Javier ; S/I ; 131481
dc.information.autorucEscuela de ingeniería ; Núñez Retamal, Felipe Eduardo ; S/I ; 131441
dc.language.isoen
dc.nota.accesoContenido completo
dc.pagina.final207981
dc.pagina.inicio207973
dc.revistaIEEE Access
dc.rightsacceso abierto
dc.subjectDecoding
dc.subjectNoise reduction
dc.subjectMultilevel converters
dc.subjectCapacitors
dc.subjectVoltage measurement
dc.subjectEstimation
dc.subjectArtificial neural networks
dc.titleDenoising and Voltage Estimation in Modular Multilevel Converters Using Deep Neural-Networkses_ES
dc.titleDenoising and Voltage Estimation in Modular Multilevel Converters Using Deep Neural-Networks
dc.typeartículo
dc.volumen8
sipa.codpersvinculados222832
sipa.codpersvinculados232465
sipa.codpersvinculados232497
sipa.codpersvinculados223382
sipa.codpersvinculados131481
sipa.codpersvinculados131441
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