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dc.contributor.authorGonzález Abreu, Artvin Darién
dc.contributor.authorSaucedo Dorantes, Juan Jose
dc.contributor.authorOsornio Rios, Roque A.
dc.contributor.authorArellano Espitia, Francisco
dc.contributor.authorDelgado Prieto, Miquel
dc.contributor.otherUniversitat Politècnica de Catalunya. Doctorat en Enginyeria Electrònica
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Enginyeria Electrònica
dc.date.accessioned2020-10-15T13:11:23Z
dc.date.issued2020
dc.identifier.citationGonzález, A.D. [et al.]. Deep learning based condition monitoring approach applied to power quality. A: IEEE International Conference on Emerging Technologies and Factory Automation. "2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA): Proceedings: Vienna, Austria - Hybrid: 08-11 September, 2020". Institute of Electrical and Electronics Engineers (IEEE), p. 1427-1430. ISBN 978-1-7281-8957-4. DOI 10.1109/ETFA46521.2020.9212076.
dc.identifier.isbn978-1-7281-8957-4
dc.identifier.urihttp://hdl.handle.net/2117/330311
dc.description.abstractCondition monitoring applied to power quality involves several techniques and procedures for the assessment of the electrical signal. Data-driven approaches are the most common methodologies supported on data and signal processing procedures. Electrical systems in factory automation become more complex with the increase of multiple load profiles connected, and unexpected electrical events can occur causing the appearance of power quality disturbances. However, emerging technologies in the techniques related to the detection and identification of power quality disturbances are analyzed and compared according to the complexity of the current electrical system, that is, including simple and combined disturbances. These new technologies allow developing more cyber-physical systems to process the new methodologies for condition monitoring. Thus, in this study, a deep learning-based approach for the identification of power quality disturbances is implemented and their performance analyzed in front of classical disturbances defined by the International standards considered in the related literature.
dc.description.sponsorshipThis research work has been partially supported by FOFIUAQ-2018 FIN 201812 and CONACyT doctoral scholarship number 735042. This work was also supported in part by the European Regional Development Fund from the European Union in the FEDER Operative Programme framework of Catalonia 2014-2020.
dc.format.extent4 p.
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.subjectÀrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic
dc.subject.lcshMachine translating
dc.subject.otherPower quality
dc.subject.otherCondition monitoring
dc.subject.otherDeep learning
dc.titleDeep learning based condition monitoring approach applied to power quality
dc.typeConference report
dc.subject.lemacAprenentatge automàtic
dc.contributor.groupUniversitat Politècnica de Catalunya. MCIA - Motion Control and Industrial Applications Research Group
dc.identifier.doi10.1109/ETFA46521.2020.9212076
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttps://ieeexplore.ieee.org/document/9212076
dc.rights.accessRestricted access - publisher's policy
local.identifier.drac29516697
dc.description.versionPostprint (published version)
dc.date.lift10000-01-01
local.citation.authorGonzález, A.D.; Saucedo, J.; Osornio, R.; Arellano, F.; Delgado Prieto, M.
local.citation.contributorIEEE International Conference on Emerging Technologies and Factory Automation
local.citation.publicationName2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA): Proceedings: Vienna, Austria - Hybrid: 08-11 September, 2020
local.citation.startingPage1427
local.citation.endingPage1430


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