Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/22974
DC FieldValueLanguage
dc.contributor.authorKaragiannopoulos, Stavros-
dc.contributor.authorVasilakis, Athanasios-
dc.contributor.authorKotsampopoulos, Panos-
dc.contributor.authorHatziargyriou, Nikos-
dc.contributor.authorAristidou, Petros-
dc.contributor.authorHug, Gabriela-
dc.date.accessioned2021-09-06T08:30:20Z-
dc.date.available2021-09-06T08:30:20Z-
dc.date.issued2021-05-02-
dc.identifier.citationEnergies, 2021, vol. 14, no. 10, articl. no. 2837en_US
dc.identifier.issn19961073-
dc.identifier.urihttps://hdl.handle.net/20.500.14279/22974-
dc.description.abstractLately, data-driven algorithms have been proposed to design local controls for Distributed Generators (DGs) that can emulate the optimal behaviour without any need for communication or centralised control. The design is based on historical data, advanced off-line optimization techniques and machine learning methods, and has shown great potential when the operating conditions are similar to the training data. However, safety issues arise when the real-time conditions start to drift away from the training set, leading to the need for online self-adapting algorithms and experimental verification of data-driven controllers. In this paper, we propose an online self-adapting algorithm that adjusts the DG controls to tackle local power quality issues. Furthermore, we provide experimental verification of the data-driven controllers through power Hardware-in-the-Loop experiments using an industrial inverter. The results presented for a low-voltage distribution network show that data-driven schemes can emulate the optimal behaviour and the online modification scheme can mitigate local power quality issues.en_US
dc.formatpdfen_US
dc.language.isoenen_US
dc.relation.ispartofEnergiesen_US
dc.rights© by the authors. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.en_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectData-driven control designen_US
dc.subjectActive distribution networksen_US
dc.subjectOPFen_US
dc.subjectMachine learningen_US
dc.subjectHardware-in-the-loopen_US
dc.titleExperimental verification of self-adapting data-driven controllers in active distribution gridsen_US
dc.typeArticleen_US
dc.collaborationETH Zurichen_US
dc.collaborationNational Technical University Of Athensen_US
dc.collaborationCyprus University of Technologyen_US
dc.subject.categoryComputer and Information Sciencesen_US
dc.journalsOpen Accessen_US
dc.countryCyprusen_US
dc.countryGreeceen_US
dc.countrySwitzerlanden_US
dc.subject.fieldNatural Sciencesen_US
dc.publicationPeer Revieweden_US
dc.identifier.doi10.3390/en14102837en_US
dc.identifier.scopus2-s2.0-85106870574-
dc.identifier.urlhttps://api.elsevier.com/content/abstract/scopus_id/85106870574-
dc.relation.issue10en_US
dc.relation.volume14en_US
cut.common.academicyear2020-2021en_US
item.grantfulltextopen-
item.languageiso639-1en-
item.cerifentitytypePublications-
item.openairecristypehttp://purl.org/coar/resource_type/c_6501-
item.openairetypearticle-
item.fulltextWith Fulltext-
crisitem.journal.journalissn1996-1073-
crisitem.journal.publisherMultidisciplinary Digital Publishing Institute-
crisitem.author.deptDepartment of Electrical Engineering, Computer Engineering and Informatics-
crisitem.author.facultyFaculty of Engineering and Technology-
crisitem.author.orcid0000-0003-4429-0225-
crisitem.author.parentorgFaculty of Engineering and Technology-
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