Gaussian process latent force models for virtual sensing in a monopile-based offshore wind turbine
Zou, J.; Cicirello, A.; Iliopoulos, A.; Lourens, E.-M. (2023). Gaussian process latent force models for virtual sensing in a monopile-based offshore wind turbine, in: Rizzo, P. et al. European Workshop on Structural Health Monitoring. EWSHM 2022 - Volume 1. pp. 290-298. https://dx.doi.org/10.1007/978-3-031-07254-3_29
In: Rizzo, P.; Milazzo, A. (Ed.) (2023). European Workshop on Structural Health Monitoring. EWSHM 2022 - Volume 1. Springer: Cham. ISBN 978-3-031-07253-6; e-ISBN 978-3-031-07254-3. XXI, 930 pp. https://dx.doi.org/10.1007/978-3-031-07254-3, meer
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| Author keywords |
Offshore wind turbines; Virtual sensing; Bayesian inference; Gaussian process |
| Auteurs | | Top |
- Zou, J.
- Cicirello, A.
- Iliopoulos, A., meer
- Lourens, E.-M.
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| Abstract |
Fatigue assessment in offshore wind turbine support structures requires the monitoring of strains below the mudline, where the highest bending moments occur. However, direct measurement of these strains is generally impractical. This paper presents the validation of a virtual sensing technique based on the Gaussian process latent force model for dynamic strain monitoring. The dataset, taken from an operating near-shore turbine in the Westermeerwind Park in the Netherlands, provides a unique opportunity for validation of strain estimates at locations below the mudline using strain gauges embedded within the monopile foundation. |
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