Articles | Volume 9, issue 11
https://doi.org/10.5194/wes-9-2039-2024
© 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-9-2039-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
04 Nov 2024
Research article |
| 04 Nov 2024
| 04 Nov 2024
Data-driven surrogate model for wind turbine damage equivalent load
Department of Mechanical Engineering, Institute for Integrated Energy Systems, University of Victoria, Victoria, British Columbia, Canada
Curran Crawford
Department of Mechanical Engineering, Institute for Integrated Energy Systems, University of Victoria, Victoria, British Columbia, Canada
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Short summary
This journal paper focuses on developing surrogate models for predicting the damage equivalent load (DEL) on wind turbines without needing extensive aeroelastic simulations. The study emphasizes the development of a sequential machine learning architecture for this purpose. The study also explores implementing simplified wake models and transfer learning to enhance the models' prediction capabilities in various wind conditions.
This journal paper focuses on developing surrogate models for predicting the damage equivalent...
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