Towards an integrative framework for digital twins in wind power

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dc.contributor.authorSiddiqui, M. Salman
dc.contributor.authorKeprate, Arvind
dc.contributor.authorYang, Liang
dc.contributor.authorMalmedal, Tiril
dc.date.accessioned2024-02-21T11:33:36Z
dc.date.available2024-02-21T11:33:36Z
dc.date.issued2024-02-01
dc.description.abstractThe present global climate crisis necessitates urgent integration of sustainable and renewable energy resources, coupled with digital technology. Renewable energy stands out as a viable solution, and among the various renewable energy sources, wind power is believed to play a crucial role in this transition. In the era of industrial digitalization, implementing smart monitoring and operation becomes a vital step toward optimizing resource utilization. Consequently, the application of Digital Twins (DT) emerges as a promising approach to enhance power output in the wind energy sector. DTs for energy systems encompass multiple areas of study, such as smart monitoring, big data technology, and advanced physical modeling. While several frameworks exist for structuring DTs, few standardized methods have been established based on the experience gained. To address this gap, the present research proposes an integrative development framework for DTs, tailored explicitly to the aerodynamics of wind turbines, to ensure their successful operation throughout the entire lifecycle, from aggregation to performing actions. A seven-step framework is presented, which identifies the potential components and methods required to create a fully developed DT.en_UK
dc.identifier.citationSiddiqui MS, Keprate A, Yang L, Malmedal T, (2023) Towards an integrative framework for digital twins in wind power. In 2023 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM). 18-21 December, Singapore, pp. 264-268en_UK
dc.identifier.eisbn979-8-3503-2316-0
dc.identifier.isbn979-8-3503-2315-3
dc.identifier.urihttps://doi.org/10.1109/IEEM58616.2023.10406340
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/20838
dc.language.isoenen_UK
dc.publisherIEEEen_UK
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectDTen_UK
dc.subjectWind turbineen_UK
dc.subjectRenewable energyen_UK
dc.subjectArtificial Intelligenceen_UK
dc.titleTowards an integrative framework for digital twins in wind poweren_UK
dc.typeConference paperen_UK

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