Publicação
Clustering of wind speed time series as a tool for wind farm diagnosis
| datacite.subject.fos | Ciências Naturais::Matemáticas | |
| dc.contributor.author | Martins, Ana | |
| dc.contributor.author | Vaz, Daniel | |
| dc.contributor.author | Silva, Tiago | |
| dc.contributor.author | Cardoso, Margarida | |
| dc.contributor.author | Carvalho, Alda | |
| dc.date.accessioned | 2026-02-02T14:30:06Z | |
| dc.date.available | 2026-02-02T14:30:06Z | |
| dc.date.issued | 2024 | |
| dc.description.abstract | In 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. | eng |
| dc.description.sponsorship | This research was funded by the Portuguese Foundation for Science and Technology (FCT, IP) under the projects “Fluid–structure interaction for functional assessment of ascending aortic aneurysms: a biomechanical-based approach towards clinical practice” (AneurysmTool) DOI: 10.54499/PTDC/EMD-EMD/1230/2021; UID/00667: Unidade de Investigação e Desenvolvimento em Engenharia Mecânica e Industrial (UNIDEMI); R. Valente Ph.D. grant 2022.12223.BD. A. Carvalho was partially supported by national funds through FCT- Fundação para a Ciência e a Tecnologia, I.P., in the framework of the unit ISEG Research; UID/06522/2025. A. C. Tomás was supported by Projetos de Investigação Clínica CUF Academic Center 2024. | |
| dc.identifier.citation | Martins, A. A., Vaz, D. C., Silva, T. A. N., Cardoso, M., & Carvalho, A. (2024). Clustering of Wind Speed Time Series as a Tool for Wind Farm Diagnosis. Mathematical and Computational Applications, 29(3), 35. https://doi.org/10.3390/mca29030035 | |
| dc.identifier.doi | 10.3390/mca29030035 | |
| dc.identifier.uri | http://hdl.handle.net/10400.2/21119 | |
| dc.language.iso | eng | |
| dc.peerreviewed | yes | |
| dc.publisher | MDPI | |
| dc.relation | Fluid-structure interaction for functional assessment of ascending aortic aneurysms: a biomechanical-based approach toward clinical practice | |
| dc.relation | ISEG Research in Economics and Management | |
| dc.relation.hasversion | https://www.mdpi.com/2297-8747/29/3/35 | |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | |
| dc.subject | Time series | |
| dc.subject | Wind data | |
| dc.subject | Clustering | |
| dc.subject | K-medoids | |
| dc.subject | COMB distance | |
| dc.subject | Visual interpretation tools | |
| dc.subject | Wind farm diagnosis | |
| dc.title | Clustering of wind speed time series as a tool for wind farm diagnosis | eng |
| dc.type | journal article | |
| dspace.entity.type | Publication | |
| oaire.awardTitle | Fluid-structure interaction for functional assessment of ascending aortic aneurysms: a biomechanical-based approach toward clinical practice | |
| oaire.awardTitle | ISEG Research in Economics and Management | |
| oaire.awardURI | http://hdl.handle.net/10400.2/21116 | |
| oaire.awardURI | http://hdl.handle.net/10400.2/21117 | |
| oaire.citation.issue | 3 | |
| oaire.citation.title | Mathematical and Computational Applications | |
| oaire.citation.volume | 29 | |
| oaire.fundingStream | Concurso de Projetos IC&DT em Todos os Domínios Científicos | |
| oaire.fundingStream | Avaliação UID 2023/2024 PRR | |
| oaire.version | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |
| person.familyName | Carvalho | |
| person.givenName | Alda | |
| person.identifier | AAA-4372-2021 | |
| person.identifier.ciencia-id | FD18-CBDD-B7C7 | |
| person.identifier.orcid | 0000-0003-2642-4947 | |
| person.identifier.scopus-author-id | 25027091800 | |
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