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Clustering of Wind Speed Time Series as a Tool for Wind Farm Diagnosis

2024-05-09Artigo científico Acesso aberto
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dc.contributor.authorMartins, Ana Alexandra
dc.contributor.authorVaz, Daniel C.
dc.contributor.authorSilva, Tiago A. N.
dc.contributor.authorCardoso, Margarida
dc.contributor.authorCarvalho, Alda
dc.contributor.institutionUNIDEMI - Unidade de Investigação e Desenvolvimento em Engenharia Mecânica e Industrial
dc.contributor.institutionDEMI - Departamento de Engenharia Mecânica e Industrial
dc.contributor.pblMDPI - Multidisciplinary Digital Publishing Institute
dc.date.accessioned2024-09-23T22:23:21Z
dc.date.available2024-09-23T22:23:21Z
dc.date.issued2024-05-09
dc.descriptionThis work was developed and financially supported under the framework of project IPL/2022/VS_FGM_ISEL. Publisher Copyright: © 2024 by the authors.
dc.description.abstractIn several industrial fields, environmental and operational data are acquired with numerous purposes, potentially generating a huge quantity of data containing valuable information for management actions. This work proposes a methodology for clustering time series based on the K-medoids algorithm using a convex combination of different time series correlation metrics, the COMB distance. The multidimensional scaling procedure is used to enhance the visualization of the clustering results, and a matrix plot display is proposed as an efficient visualization tool to interpret the COMB distance components. This is a general-purpose methodology that is intended to ease time series interpretation; however, due to the relevance of the field, this study explores the clustering of time series judiciously collected from data of a wind farm located on a complex terrain. Using the COMB distance for wind speed time bands, clustering exposes operational similarities and dissimilarities among neighboring turbines which are influenced by the turbines’ relative positions and terrain features and regarding the direction of oncoming wind. In a significant number of cases, clustering does not coincide with the natural geographic grouping of the turbines. A novel representation of the contributing distances—the COMB distance matrix plot—provides a quick way to compare pairs of time bands (turbines) regarding various features.en
dc.description.versionpublishersversion
dc.description.versionpublished
dc.format.extent16
dc.format.extent3519539
dc.identifier.doi10.3390/mca29030035
dc.identifier.issn1300-686X
dc.identifier.otherPURE: 99836128
dc.identifier.otherPURE UUID: fe6de658-30f8-4687-bcc8-8d09e8710232
dc.identifier.otherScopus: 85196837227
dc.identifier.otherWOS: 001256699000001
dc.identifier.otherORCID: /0000-0002-5065-7938/work/168128158
dc.identifier.urihttp://hdl.handle.net/10362/172283
dc.identifier.urlhttps://www.scopus.com/pages/publications/85196837227
dc.language.isoeng
dc.peerreviewedyes
dc.relationFunding Information: info:eu-repo/grantAgreement/FCT/Concurso de avaliação no âmbito do Programa Plurianual de Financiamento de Unidades de I&D (2017%2F2018) - Financiamento Base/UIDB%2F00667%2F2020/PT
dc.relationinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F00667%2F2020/PT
dc.relationResearch and Development Unit for Mechanical and Industrial Engineering
dc.relationBusiness Research Unit - BRU-IUL
dc.relationResearch in Economics and Mathematics
dc.subjectclustering
dc.subjectCOMB distance
dc.subjectK-medoids
dc.subjecttime series
dc.subjectvisual interpretation tools
dc.subjectwind data
dc.subjectwind farm diagnosis
dc.subjectGeneral Engineering
dc.subjectComputational Mathematics
dc.subjectApplied Mathematics
dc.subjectSDG 7 - Affordable and Clean Energy
dc.titleClustering of Wind Speed Time Series as a Tool for Wind Farm Diagnosisen
dc.typejournal article
degois.publication.issue3
degois.publication.titleMathematical and Computational Applications
degois.publication.volume29
dspace.entity.typePublication
oaire.awardNumberUIDP/00667/2020
oaire.awardNumberUIDB/00315/2020
oaire.awardNumberUIDB/05069/2020
oaire.awardTitleResearch and Development Unit for Mechanical and Industrial Engineering
oaire.awardTitleBusiness Research Unit - BRU-IUL
oaire.awardTitleResearch in Economics and Mathematics
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F00667%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00315%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F05069%2F2020/PT
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccess
relation.isProjectOfPublication537c7b42-78a4-457e-9e52-3c3c32cc9f04
relation.isProjectOfPublication536026e1-acfa-4325-8d21-41543941bc14
relation.isProjectOfPublication5ef57d48-6cfd-46c2-bd58-a8d3b2e22afa
relation.isProjectOfPublication.latestForDiscovery537c7b42-78a4-457e-9e52-3c3c32cc9f04

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