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

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dc.contributor.authorLANGARICA, SAÚL
dc.contributor.authorPIZARRO, GERMÁN
dc.contributor.authorPOBLETE, PABLO M.
dc.contributor.authorPEREDA, JAVIER
dc.contributor.authorRODRIGUEZ, JOSE
dc.contributor.authorNÚÑEZ, FELIPE
dc.date.accessioned2021-11-19T19:04:49Z
dc.date.available2021-11-19T19:04:49Z
dc.date.issued2020-11
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. © 2013 IEEE.es
dc.description.sponsorshipIndexación: Scopuses
dc.identifier.citationIEEE Access Open AccessVolume 8, Pages 207973 - 2079812020 Article number 9261401es
dc.identifier.issn21693536
dc.identifier.urihttp://repositorio.unab.cl/xmlui/handle/ria/20989
dc.language.isoenes
dc.publisherInstitute of Electrical and Electronics Engineers Inc.es
dc.subjectcyber-physical systems; Modular multilevel converters; neural networkses
dc.titleDenoising and Voltage Estimation in Modular Multilevel Converters Using Deep Neural-Networkses
dc.typeArtículoes
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