Articles | Volume 9, issue 1
https://doi.org/10.5194/wes-9-1-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-9-1-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
08 Jan 2024
Research article |
| 08 Jan 2024
| 08 Jan 2024
A digital twin solution for floating offshore wind turbines validated using a full-scale prototype
Emmanuel Branlard
CORRESPONDING AUTHOR
National Renewable Energy Laboratory, Golden, CO 80401, USA
Jason Jonkman
National Renewable Energy Laboratory, Golden, CO 80401, USA
Cameron Brown
Stiesdal Offshore A/S, Copenhagen, Denmark
Jiatian Zhang
Stiesdal Offshore A/S, Copenhagen, Denmark
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Total article views: 3,298 (including HTML, PDF, and XML)
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20 citations as recorded by crossref.
- Graph neural networks for virtual sensing in complex systems: Addressing heterogeneous temporal dynamics M. Zhao et al. 10.1016/j.ymssp.2025.112544
- Enhancing Smart Grid Security and Efficiency: AI, Energy Routing, and T&D Innovations (A Review) H. Ishfaq et al. 10.3390/en18174747
- Design of Virtual Sensors for a Pyramidal Weathervaning Floating Wind Turbine H. del Pozo Gonzalez et al. 10.3390/jmse13081411
- Data-driven surrogate model for wind turbine damage equivalent load R. Haghi & C. Crawford 10.5194/wes-9-2039-2024
- On the modeling errors of digital twins for load monitoring and fatigue assessment in wind turbine drivetrains F. Mehlan & A. Nejad 10.5194/wes-10-417-2025
- Digital Twin-Based Approach for a Multi-Objective Optimal Design of Wind Turbine Gearboxes C. Llopis-Albert et al. 10.3390/math12091383
- Offshore wind turbine tower design and optimization: A review and AI-driven future directions J. Alves Ribeiro et al. 10.1016/j.apenergy.2025.126294
- A Joint Method on Dynamic States Estimation for Digital Twin of Floating Offshore Wind Turbines H. Xie et al. 10.3390/jmse13101981
- Strain virtual sensor for offshore wind turbine jacket supports: A time series transformer approach validated with Alpha Ventus wind farm data Á. Encalada-Dávila et al. 10.1016/j.ymssp.2025.112653
- Sustainable Energy and Exergy Analysis in Offshore Wind Farms Using Machine Learning: A Systematic Review H. Soltani Motlagh et al. 10.3390/eng6060105
- Enhancing Reliability in Floating Offshore Wind Turbines through Digital Twin Technology: A Comprehensive Review B. Chen et al. 10.3390/en17081964
- A wind turbine digital shadow with tower and blade degrees of freedom - Preliminary results and comparison with a simple tower fore-aft model H. Hoghooghi et al. 10.1088/1742-6596/2767/3/032026
- Wind Turbine SCADA Data Imbalance: A Review of Its Impact on Health Condition Analyses and Mitigation Strategies A. Oliveira-Filho et al. 10.3390/en18010059
- Real-Time Digital Twin for Structural Health Monitoring of Floating Offshore Wind Turbines A. Pastor-Sanchez et al. 10.3390/jmse13101953
- Assessment of multi-fidelity hydrodynamic models of floating wind turbines in extreme wave events J. Fontaine et al. 10.1016/j.renene.2025.124639
- Blockchain-based intelligent equipment assessment in manufacturing industry T. Ahanger et al. 10.1007/s10845-025-02621-5
- Considerations for the global commercialization of floating offshore wind energy A. Robertson et al. 10.1038/s44359-025-00093-7
- A Review of Digital Twinning Applications for Floating Offshore Wind Turbines: Insights, Innovations, and Implementation I. Taze et al. 10.3390/en18133369
- Towards digital twinning: Input-state-parameter estimation through extended MVU filter for systems without direct feedthrough using computer vision M. Masanes Didyk et al. 10.1016/j.ymssp.2025.112557
- Experimental validation of a Kalman observer using linearized OpenFAST and a fully instrumented 1:70 model I. Ammerman et al. 10.1002/we.2915
18 citations as recorded by crossref.
- Graph neural networks for virtual sensing in complex systems: Addressing heterogeneous temporal dynamics M. Zhao et al. 10.1016/j.ymssp.2025.112544
- Enhancing Smart Grid Security and Efficiency: AI, Energy Routing, and T&D Innovations (A Review) H. Ishfaq et al. 10.3390/en18174747
- Design of Virtual Sensors for a Pyramidal Weathervaning Floating Wind Turbine H. del Pozo Gonzalez et al. 10.3390/jmse13081411
- Data-driven surrogate model for wind turbine damage equivalent load R. Haghi & C. Crawford 10.5194/wes-9-2039-2024
- On the modeling errors of digital twins for load monitoring and fatigue assessment in wind turbine drivetrains F. Mehlan & A. Nejad 10.5194/wes-10-417-2025
- Digital Twin-Based Approach for a Multi-Objective Optimal Design of Wind Turbine Gearboxes C. Llopis-Albert et al. 10.3390/math12091383
- Offshore wind turbine tower design and optimization: A review and AI-driven future directions J. Alves Ribeiro et al. 10.1016/j.apenergy.2025.126294
- A Joint Method on Dynamic States Estimation for Digital Twin of Floating Offshore Wind Turbines H. Xie et al. 10.3390/jmse13101981
- Strain virtual sensor for offshore wind turbine jacket supports: A time series transformer approach validated with Alpha Ventus wind farm data Á. Encalada-Dávila et al. 10.1016/j.ymssp.2025.112653
- Sustainable Energy and Exergy Analysis in Offshore Wind Farms Using Machine Learning: A Systematic Review H. Soltani Motlagh et al. 10.3390/eng6060105
- Enhancing Reliability in Floating Offshore Wind Turbines through Digital Twin Technology: A Comprehensive Review B. Chen et al. 10.3390/en17081964
- A wind turbine digital shadow with tower and blade degrees of freedom - Preliminary results and comparison with a simple tower fore-aft model H. Hoghooghi et al. 10.1088/1742-6596/2767/3/032026
- Wind Turbine SCADA Data Imbalance: A Review of Its Impact on Health Condition Analyses and Mitigation Strategies A. Oliveira-Filho et al. 10.3390/en18010059
- Real-Time Digital Twin for Structural Health Monitoring of Floating Offshore Wind Turbines A. Pastor-Sanchez et al. 10.3390/jmse13101953
- Assessment of multi-fidelity hydrodynamic models of floating wind turbines in extreme wave events J. Fontaine et al. 10.1016/j.renene.2025.124639
- Blockchain-based intelligent equipment assessment in manufacturing industry T. Ahanger et al. 10.1007/s10845-025-02621-5
- Considerations for the global commercialization of floating offshore wind energy A. Robertson et al. 10.1038/s44359-025-00093-7
- A Review of Digital Twinning Applications for Floating Offshore Wind Turbines: Insights, Innovations, and Implementation I. Taze et al. 10.3390/en18133369
2 citations as recorded by crossref.
- Towards digital twinning: Input-state-parameter estimation through extended MVU filter for systems without direct feedthrough using computer vision M. Masanes Didyk et al. 10.1016/j.ymssp.2025.112557
- Experimental validation of a Kalman observer using linearized OpenFAST and a fully instrumented 1:70 model I. Ammerman et al. 10.1002/we.2915
Latest update: 29 Oct 2025
Short summary
In this work, we implement, verify, and validate a physics-based digital twin solution applied to a floating offshore wind turbine. The article present methods to obtain reduced-order models of floating wind turbines. The models are used to form a digital twin which combines measurements from the TetraSpar prototype (a full-scale floating offshore wind turbine) to estimate signals that are not typically measured.
In this work, we implement, verify, and validate a physics-based digital twin solution applied...
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