Probabilistic surrogate modeling of damage equivalent loads on onshore and offshore wind turbines using mixture density networks

Singh, Deepali; Dwight, Richard; Viré, Axelle

doi:https://doi.org/10.5194/wes-9-1885-2024

Articles | Volume 9, issue 10

https://doi.org/10.5194/wes-9-1885-2024

© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/wes-9-1885-2024

© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 9, issue 10

Research article

|

07 Oct 2024

Research article |

| 07 Oct 2024

Probabilistic surrogate modeling of damage equivalent loads on onshore and offshore wind turbines using mixture density networks

Deepali Singh, Richard Dwight, and Axelle Viré

Data sets

Training and validation datasets for training probabilistic machine learning models on NREL's 10-MW reference wind turbine Deepali Singh https://doi.org/10.4121/21939995

Short summary

The selection of a suitable site for the installation of a wind turbine plays an important role in ensuring a safe operating lifetime of the structure. In this study, we show that mixture density networks can accelerate this process by inferring functions from data that can accurately map the environmental conditions to the loads but also propagate the uncertainty from the inflow to the response.