Session: 09-01-10: Offshore Wind Energy - Data science and Digital twins

Paper Number: 103112

103112 - Demonstration of a Standalone, Descriptive, and Predictive Digital Twin of a Floating Offshore Wind Turbine 

Offshore wind energy production will significantly increase in the coming years. Especially floating offshore wind farms allow for tapping the enormous wind resources available in deep waters. However, their remote locations result in several challenges connected to operation and maintenance, including a harsh environment, a potential lack of local expertise, expensive on-site inspections, and long response time to unexpected downtime. Digital twin is an upcoming technology that addresses these challenges by making it possible to perform significant parts of the operation and maintenance process remotely. The understanding of what features precisely are and are not part of a digital twin varies widely.
Conventional scales, such as the technology readiness levels, cannot account for these varying capabilities of the digital twin. To this end, a scale of 0-5 (0-standalone, 1- descriptive, 2-diagnostic, 3-predictive, 4-prescriptive, 5-autonomous) has recently been proposed to quantify the capability of any digital twin. In this work, we give a conceptual digital twin and its capability level scales in the context of wind energy. Furthermore, we demonstrate a standalone and descriptive digital twin of an operational floating offshore wind turbine. The standalone digital twin of the turbine consists of the virtual representation of the turbine and its operating environment. While at this level the digital twin does not evolve with the physical turbine, it can be used during the planning-, design-, and construction phases. At the next level, the descriptive digital twin is built upon the standalone digital twin by enhancing the latter with a real-time data stream from the turbine. All the data is visualized in virtual reality for informed decision-making. Besides being used for data bundling and visualization, the descriptive digital twin forms the basis for enabling higher capability levels by implementing diagnostic, predictive, prescriptive, and autonomous tools. The work also highlights the challenges in developing higher capability level digital twins and recommends potential solutions. Finally, the technology is discussed in a much wider context of ocean engineering.

Presenting Author: Florian Stadtmann Norwegian University of Science and Technology

Presenting Author Biography: - B.Sc. and M.Sc. in Physics at RWTH Aachen, Germany
- Currently Ph.D. candidate at the Department of Engineering Cybernetics, NTNU Trondheim, Norway
- Working on digital twins with application to wind energy

Authors:

Florian Stadtmann Norwegian University of Science and Technology
Henrik Andreas Gusdal Wassertheurer Norwegian University of Science and Technology
Adil Rasheed Norwegian University of Science and Technology