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dc.contributor.authorUgarte Valdivielso, Jone
dc.contributor.authorAizpurua Unanue, Jose Ignacio
dc.contributor.authorBarrenetxea, Manex
dc.date.accessioned2024-06-11T10:00:58Z
dc.date.available2024-06-11T10:00:58Z
dc.date.issued2024
dc.identifier.issn1937-4208en
dc.identifier.otherhttps://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=177527en
dc.identifier.urihttps://hdl.handle.net/20.500.11984/6516
dc.description.abstractTransformers are one of the key assets in AC distribution grids and renewable power integration. During transformer energization inrush currents appear, which lead to transformer degradation and can cause grid instability events. These inrush currents are a consequence of the transformer's magnetic core saturation during its connection to the grid. Transformer cores are normally modelled by the Jiles-Atherton (JA) model which contains five parameters. These parameters can be estimated by metaheuristic-based search algorithms. The parameter initialization of these algorithms plays an important role in the algorithm convergence. The most popular strategy used for JA parameter initialization is a random uniform distribution. However, techniques such as parameter initialization by Probability Density Functions (PDFs) have shown to improve accuracy over random methods. In this context, this research work presents a framework to assess the impact of different parameter initialization strategies on the performance of the JA parameter estimation for inrush current studies. Depending on available data and expert knowledge, uncertainty levels are modelled with different PDFs. Moreover, three different metaheuristic-search algorithms are employed on two different core materials and their accuracy and computational time are compared. Results show an improvement in the accuracy and computational time of the metaheuristic-based algorithms when PDF parameter initialization is used.en
dc.language.isoengen
dc.publisherIEEEen
dc.rights© 2024 IEEEen
dc.subjectinrush currenten
dc.subjectTransformersen
dc.subjectJiles-Atherton modelen
dc.subjectmetaheuristic-based searchen
dc.subjectProbability density functionen
dc.subjectuncertaintyen
dc.subjectODS 9 Industria, innovación e infraestructuraes
dc.titleUncertainty Distribution Assessment of Jiles-Atherton Parameter Estimation for Inrush Current Studiesen
dcterms.accessRightshttp://purl.org/coar/access_right/c_abf2en
dcterms.sourceIEEE Transactions on Power Deliveryen
local.contributor.groupAccionamientos aplicados a la tracción y a la generación de energía eléctricaes
local.contributor.groupSistemas electrónicos de potencia aplicados al control de la energía eléctricaes
local.description.peerreviewedtrueen
local.identifier.doihttps://doi.org/10.1109/TPWRD.2024.3398790en
local.contributor.otherinstitutionhttps://ror.org/01cc3fy72en
oaire.format.mimetypeapplication/pdfen
oaire.file$DSPACE\assetstoreen
oaire.resourceTypehttp://purl.org/coar/resource_type/c_6501en
oaire.versionhttp://purl.org/coar/version/c_ab4af688f83e57aaen
oaire.funderNameGobierno de Españaen
oaire.funderIdentifierhttps://ror.org/038jjxj40 / http://data.crossref.org/fundingdata/funder/10.13039/501100010198en
oaire.fundingStreamConvocatoria 2021. Programa Estatal para Impulsar la Investigación Científico-Técnica y su Transferencia, del Plan Estatal de Investigación Científica, Técnica y de Innovación 2021-2023en
oaire.awardNumberCPP2021-008580en
oaire.awardTitleModelización y Diagnóstico de Transformadores (MODITRANS)en
oaire.awardURISin informaciónen


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