Digital Twins in Wind Energy: Emerging Technologies and Industry-Informed Future Directions
This article presents a comprehensive overview of the digital twin technology and its capability levels, with a specific focus on its applications in the wind energy industry. It consolidates the definitions of digital twin and its capability levels on a scale from 0-5; 0-standalone, 1-descriptive,...
| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10268901/ |
| _version_ | 1797660768105136128 |
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| author | Florian Stadtmann Adil Rasheed Trond Kvamsdal Kjetil Andre Johannessen Omer San Konstanze Kolle John Olav Tande Idar Barstad Alexis Benhamou Thomas Brathaug Tore Christiansen Anouk-Letizia Firle Alexander Fjeldly Lars Froyd Alexander Gleim Alexander Hoiberget Catherine Meissner Guttorm Nygard Jorgen Olsen Havard Paulshus Tore Rasmussen Elling Rishoff Francesco Scibilia John Olav Skogas |
| author_facet | Florian Stadtmann Adil Rasheed Trond Kvamsdal Kjetil Andre Johannessen Omer San Konstanze Kolle John Olav Tande Idar Barstad Alexis Benhamou Thomas Brathaug Tore Christiansen Anouk-Letizia Firle Alexander Fjeldly Lars Froyd Alexander Gleim Alexander Hoiberget Catherine Meissner Guttorm Nygard Jorgen Olsen Havard Paulshus Tore Rasmussen Elling Rishoff Francesco Scibilia John Olav Skogas |
| author_sort | Florian Stadtmann |
| collection | DOAJ |
| description | This article presents a comprehensive overview of the digital twin technology and its capability levels, with a specific focus on its applications in the wind energy industry. It consolidates the definitions of digital twin and its capability levels on a scale from 0-5; 0-standalone, 1-descriptive, 2-diagnostic, 3-predictive, 4-prescriptive, 5-autonomous. It then, from an industrial perspective, identifies the current state of the art and research needs in the wind energy sector. It is concluded that the main challenges hindering the realization of highly capable digital twins fall into one of the four categories; standards-related, data-related, model-related, and industrial acceptance related. The article proposes approaches to the identified challenges from the perspective of research institutes and offers a set of recommendations for various stakeholders to facilitate the acceptance of the technology. The contribution of this article lies in its synthesis of the current state of knowledge and its identification of future research needs and challenges from an industry perspective, ultimately providing a roadmap for future research and development in the field of digital twin and its applications in the wind energy industry. |
| first_indexed | 2024-03-11T18:35:40Z |
| format | Article |
| id | doaj.art-3db25e99bdfa4a6385b8d19f703f6eb1 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-11T18:35:40Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-3db25e99bdfa4a6385b8d19f703f6eb12023-10-12T23:01:09ZengIEEEIEEE Access2169-35362023-01-011111076211079510.1109/ACCESS.2023.332132010268901Digital Twins in Wind Energy: Emerging Technologies and Industry-Informed Future DirectionsFlorian Stadtmann0https://orcid.org/0009-0003-5155-0870Adil Rasheed1https://orcid.org/0000-0003-2690-983XTrond Kvamsdal2https://orcid.org/0000-0002-1615-7649Kjetil Andre Johannessen3Omer San4Konstanze Kolle5John Olav Tande6https://orcid.org/0000-0003-1562-8928Idar Barstad7Alexis Benhamou8https://orcid.org/0000-0001-8566-2932Thomas Brathaug9Tore Christiansen10Anouk-Letizia Firle11Alexander Fjeldly12https://orcid.org/0009-0005-1901-1310Lars Froyd13https://orcid.org/0009-0006-1126-0600Alexander Gleim14https://orcid.org/0009-0001-9893-0614Alexander Hoiberget15Catherine Meissner16Guttorm Nygard17Jorgen Olsen18Havard Paulshus19Tore Rasmussen20Elling Rishoff21Francesco Scibilia22John Olav Skogas23Norwegian University of Science and Technology, Trondheim, NorwayDepartment of Engineering Cybernetics, Norwegian University of Science and Technology, Trondheim, NorwayMathematics and Cybernetics, SINTEF Digital, Trondheim, NorwaySINTEF Digital, Trondheim, NorwayDepartment of Mechanical, Aerospace and Biomedical Engineering, University of Tennessee, Knoxville, TN, USASINTEF Energy Research, Trondheim, NorwaySINTEF Energy Research, Trondheim, NorwayNorconsult, Sandvika, NorwayTotalEnergies, Paris La Défense, FranceVard, Ålesund, NorwayDNV, Høvik, NorwaySustainable Energy Catapult Center, Stord, NorwayFORCE Technology, Hvalstad, Norway4subsea, Asker, NorwayCognite, Lysaker, NorwayEIDEL, Eisvoll, NorwayMainstream Renewable Power, Lysaker, NorwayStore Norske, Longyearbyen, NorwayStatkraft, Oslo, NorwayKongsberg Digital, Lysaker, NorwayANEO, Trondheim, NorwayDNV, Høvik, NorwayEquinor ASA, Trondheim, Rotvoll, NorwayKongsberg Maritime, Trondheim, NorwayThis article presents a comprehensive overview of the digital twin technology and its capability levels, with a specific focus on its applications in the wind energy industry. It consolidates the definitions of digital twin and its capability levels on a scale from 0-5; 0-standalone, 1-descriptive, 2-diagnostic, 3-predictive, 4-prescriptive, 5-autonomous. It then, from an industrial perspective, identifies the current state of the art and research needs in the wind energy sector. It is concluded that the main challenges hindering the realization of highly capable digital twins fall into one of the four categories; standards-related, data-related, model-related, and industrial acceptance related. The article proposes approaches to the identified challenges from the perspective of research institutes and offers a set of recommendations for various stakeholders to facilitate the acceptance of the technology. The contribution of this article lies in its synthesis of the current state of knowledge and its identification of future research needs and challenges from an industry perspective, ultimately providing a roadmap for future research and development in the field of digital twin and its applications in the wind energy industry.https://ieeexplore.ieee.org/document/10268901/Artificial intelligencedigital twinmachine learninghybrid analysis and modelingwind energy |
| spellingShingle | Florian Stadtmann Adil Rasheed Trond Kvamsdal Kjetil Andre Johannessen Omer San Konstanze Kolle John Olav Tande Idar Barstad Alexis Benhamou Thomas Brathaug Tore Christiansen Anouk-Letizia Firle Alexander Fjeldly Lars Froyd Alexander Gleim Alexander Hoiberget Catherine Meissner Guttorm Nygard Jorgen Olsen Havard Paulshus Tore Rasmussen Elling Rishoff Francesco Scibilia John Olav Skogas Digital Twins in Wind Energy: Emerging Technologies and Industry-Informed Future Directions IEEE Access Artificial intelligence digital twin machine learning hybrid analysis and modeling wind energy |
| title | Digital Twins in Wind Energy: Emerging Technologies and Industry-Informed Future Directions |
| title_full | Digital Twins in Wind Energy: Emerging Technologies and Industry-Informed Future Directions |
| title_fullStr | Digital Twins in Wind Energy: Emerging Technologies and Industry-Informed Future Directions |
| title_full_unstemmed | Digital Twins in Wind Energy: Emerging Technologies and Industry-Informed Future Directions |
| title_short | Digital Twins in Wind Energy: Emerging Technologies and Industry-Informed Future Directions |
| title_sort | digital twins in wind energy emerging technologies and industry informed future directions |
| topic | Artificial intelligence digital twin machine learning hybrid analysis and modeling wind energy |
| url | https://ieeexplore.ieee.org/document/10268901/ |
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