Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/18088
DC FieldValueLanguage
dc.contributor.authorKaragiannopoulos, Stavros-
dc.contributor.authorAristidou, Petros-
dc.contributor.authorHug, Gabriela-
dc.date.accessioned2020-03-16T18:26:34Z-
dc.date.available2020-03-16T18:26:34Z-
dc.date.issued2019-11-02-
dc.identifier.citationIEEE Transactions on Smart Grid, 2018, vol. 10, no. 6, pp.6461-6471en_US
dc.identifier.issn19493053-
dc.description.abstractThe optimal control of distribution networks often requires monitoring and communication infrastructure, either centralized or distributed. However, most of the current distribution systems lack this kind of infrastructure and rely on suboptimal, fit-and-forget, local controls to ensure the security of the network. In this paper, we propose a data-driven algorithm that uses historical data, advanced optimization techniques, and machine learning methods, to design local controls that emulate the optimal behavior without the use of any communication. We demonstrate the performance of the optimized local control on a three-phase, unbalanced, low-voltage, distribution network. The results show that our data-driven methodology clearly outperforms standard industry local control and successfully imitates an optimal-power-flow-based control.en_US
dc.formatpdfen_US
dc.language.isoenen_US
dc.relation.ispartofIEEE Transactions on Smart Griden_US
dc.rights© IEEEen_US
dc.subjectActive distribution networksen_US
dc.subjectBackward forward sweep power flowen_US
dc.subjectData-driven control designen_US
dc.subjectdecentralized controlen_US
dc.subjectdistributed energy resourcesen_US
dc.subjectmachine learningen_US
dc.subjectOPFen_US
dc.titleData-Driven Local Control Design for Active Distribution Grids Using Off-Line Optimal Power Flow and Machine Learning Techniquesen_US
dc.typeArticleen_US
dc.collaborationLeeds Universityen_US
dc.collaborationETH Zurichen_US
dc.collaborationCyprus University of Technologyen_US
dc.subject.categoryElectrical Engineering - Electronic Engineering - Information Engineeringen_US
dc.journalsOpen Accessen_US
dc.countryUnited Kingdomen_US
dc.countrySwitzerlanden_US
dc.countryCyprusen_US
dc.subject.fieldEngineering and Technologyen_US
dc.publicationPeer Revieweden_US
dc.identifier.doi10.1109/TSG.2019.2905348en_US
dc.identifier.scopus2-s2.0-85072346668-
dc.identifier.urlhttp://arxiv.org/abs/1808.01009v2-
dc.relation.issue6en_US
dc.relation.volume10en_US
cut.common.academicyear2018-2019en_US
dc.identifier.spage6461en_US
dc.identifier.epage6471en_US
item.fulltextWith Fulltext-
item.languageiso639-1en-
item.grantfulltextopen-
item.openairecristypehttp://purl.org/coar/resource_type/c_6501-
item.cerifentitytypePublications-
item.openairetypearticle-
crisitem.journal.journalissn1949-3053-
crisitem.journal.publisherIEEE-
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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