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

• URL: http://arxiv.org/abs/2304.11405v2
• Date: Sat, 14 Oct 2023 08:49:17 GMT
• Title: Digital Twins in Wind Energy: Emerging Technologies and Industry-Informed Future Directions
• Authors: Florian Stadtman, Adil Rasheed, Trond Kvamsdal, Kjetil Andr\'e Johannessen, Omer San, Konstanze K\"olle, John Olav Gi{\ae}ver Tande, Idar Barstad, Alexis Benhamou, Thomas Brathaug, Tore Christiansen, Anouk-Letizia Firle, Alexander Fjeldly, Lars Fr{\o}yd, Alexander Gleim, Alexander H{\o}iberget, Catherine Meissner, Guttorm Nyg{\aa}rd, J{\o}rgen Olsen, H{\aa}vard Paulshus, Tore Rasmussen, Elling Rishoff, Francesco Scibilia, John Olav Skog{\aa}s
• Abstract summary: 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.
• Score: 75.81393574964038
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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. The article proposes approaches to the identified challenges from the perspective of research institutes and offers a set of recommendations for diverse 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.

Related papers

• Deepfake Generation and Detection: A Benchmark and Survey [134.19054491600832]
  Deepfake is a technology dedicated to creating highly realistic facial images and videos under specific conditions. This survey comprehensively reviews the latest developments in deepfake generation and detection. We focus on researching four representative deepfake fields: face swapping, face reenactment, talking face generation, and facial attribute editing.
  arXiv  Detail & Related papers  (2024-03-26T17:12:34Z)
• Digital Twin for Wind Energy: Latest updates from the NorthWind project [0.0]
  NorthWind aims to advance cutting-edge research and innovation in wind energy. Digital twins are a virtual representation of physical assets or processes. Digital twins can enable real-time forecasting, optimization, monitoring, control and informed decision-making.
  arXiv  Detail & Related papers  (2024-02-21T22:53:39Z)
• From Digital Twins to Digital Twin Prototypes: Concepts, Formalization, and Applications [55.57032418885258]
  There is no consensual definition of what a digital twin is. Our digital twin prototype (DTP) approach supports engineers during the development and automated testing of embedded software systems.
  arXiv  Detail & Related papers  (2024-01-15T22:13:48Z)
• On the Opportunities of Green Computing: A Survey [80.21955522431168]
  Artificial Intelligence (AI) has achieved significant advancements in technology and research with the development over several decades. The needs for high computing power brings higher carbon emission and undermines research fairness. To tackle the challenges of computing resources and environmental impact of AI, Green Computing has become a hot research topic.
  arXiv  Detail & Related papers  (2023-11-01T11:16:41Z)
• Knowledge Engineering for Wind Energy [0.0]
  This article addresses the challenges faced by wind energy domain experts in converting data into domain knowledge. It highlights the role that knowledge engineering can play in the process of digital transformation of the wind energy sector.
  arXiv  Detail & Related papers  (2023-10-01T22:06:10Z)
• A Comprehensive Review of Digital Twin -- Part 1: Modeling and Twinning Enabling Technologies [11.241244950889886]
  Digital twin is an emerging technology in the era of Industry 4.0. Digital twins can model the physical world as a group of interconnected digital models. In part two of this review, the role of uncertainty quantification and optimization are discussed.
  arXiv  Detail & Related papers  (2022-08-26T15:01:26Z)
• Mapping Industry 4.0 Technologies: From Cyber-Physical Systems to Artificial Intelligence [0.0]
  The fourth industrial revolution is rapidly changing the manufacturing landscape. No clear definitions of these concepts yet exist. This work provides a clear description of technological trends and gaps.
  arXiv  Detail & Related papers  (2021-11-28T15:13:05Z)
• Federated Learning for Industrial Internet of Things in Future Industries [106.13524161081355]
  The Industrial Internet of Things (IIoT) offers promising opportunities to transform the operation of industrial systems. Recently, artificial intelligence (AI) has been widely utilized for realizing intelligent IIoT applications. Federated Learning (FL) is particularly attractive for intelligent IIoT networks by coordinating multiple IIoT devices and machines to perform AI training at the network edge.
  arXiv  Detail & Related papers  (2021-05-31T01:02:59Z)
• Drone-based AI and 3D Reconstruction for Digital Twin Augmentation [2.309018557701645]
  Digital Twin is an emerging technology at the forefront of Industry 4.0. This study aims to augment Digital Twin in buildings with the use of AI and 3D reconstruction empowered by unmanned aviation vehicles.
  arXiv  Detail & Related papers  (2021-05-20T03:31:15Z)
• Digital Twins: State of the Art Theory and Practice, Challenges, and Open Research Questions [62.67593386796497]
  This work explores the various DT features and current approaches, the shortcomings and reasons behind the delay in the implementation and adoption of digital twin. The major reasons for this delay are the lack of a universal reference framework, domain dependence, security concerns of shared data, reliance of digital twin on other technologies, and lack of quantitative metrics.
  arXiv  Detail & Related papers  (2020-11-02T19:08:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.