Digital Twins in Wind Energy: Emerging Technologies and Industry-Informed Future Directions

被引：18

作者：

Stadtmann, Florian ^{[1
]}

Rasheed, Adil ^{[2
,3
]}

Kvamsdal, Trond ^{[3
,4
]}

Johannessen, Kjetil Andre ^{[5
]}

San, Omer ^{[6
]}

Kolle, Konstanze ^{[7
]}

Tande, John Olav ^{[7
]}

Barstad, Idar ^{[8
]}

Benhamou, Alexis ^{[9
]}

Brathaug, Thomas ^{[10
]}

Christiansen, Tore ^{[11
]}

Firle, Anouk-Letizia ^{[12
]}

Fjeldly, Alexander ^{[13
]}

Froyd, Lars ^{[14
]}

Gleim, Alexander ^{[15
]}

Hoiberget, Alexander ^{[16
]}

Meissner, Catherine ^{[17
]}

Nygard, Guttorm ^{[18
]}

Olsen, Jorgen ^{[19
]}

Paulshus, Havard ^{[20
]}

Rasmussen, Tore ^{[21
]}

Rishoff, Elling ^{[11
]}

Scibilia, Francesco ^{[22
]}

Skogas, John Olav ^{[23
]}

机构：

[1] Norwegian Univ Sci & Technol, N-7034 Trondheim, Norway

[2] Norwegian Univ Sci & Technol, Dept Engn Cybernet, N-7034 Trondheim, Norway

[3] SINTEF Digital, Math & Cybernet, N-7037 Trondheim, Norway

[4] Norwegian Univ Sci & Technol, Dept Math Sci, N-7034 Trondheim, Norway

[5] SINTEF Digital, N-7037 Trondheim, Norway

[6] Univ Tennessee, Dept Mech Aerosp & Biomed Engn, Knoxville, TN 37996 USA

[7] SINTEF Energy Res, N-7465 Trondheim, Norway

[8] Norconsult, N-1338 Sandvika, Norway

[9] TotalEnergies, F-92078 Paris, France

[10] Vard, N-6008 Alesund, Norway

[11] DNV, N-1363 Hovik, Norway

[12] Sustainable Energy Catapult Ctr, N-5412 Stord, Norway

[13] FORCE Technol, N-1395 Hvalstad, Norway

[14] 4subsea, N-1383 Asker, Norway

[15] Cognite, N-1366 Lysaker, Norway

[16] EIDEL, N-2080 Eisvoll, Norway

[17] Mainstream Renewable Power, N-1366 Lysaker, Norway

[18] Store Norske, N-9171 Longyearbyen, Norway

[19] Statkraft, N-0216 Oslo, Norway

[20] Kongsberg Digital, N-1366 Lysaker, Norway

[21] ANEO, N-7031 Trondheim, Norway

[22] Equinor ASA, N-7005 Trondheim, Norway

[23] Kongsberg Maritime, N-7005 Trondheim, Norway

来源：

IEEE ACCESS | 2023年 / 11卷

关键词：

Artificial intelligence; digital twin; machine learning; hybrid analysis and modeling; wind energy; NEURAL-NETWORKS; MODEL; TURBINES; FARM; OPTIMIZATION; PLACEMENT; POWER; LIDAR; DEGRADATION; ALGORITHM;

D O I：

10.1109/ACCESS.2023.3321320

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：110762 / 110795

页数：34

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