Gaussian Process Latent Force Models for Virtual Sensing in a Monopile-Based Offshore Wind Turbine

Conference Paper (2022)
Author(s)

J. Zou (TU Delft - Offshore Engineering)

A. Cicirello (TU Delft - Mechanics and Physics of Structures)

Alexandros Iliopoulos (Siemens)

Eliz-Mari Lourens (TU Delft - Offshore Engineering, TU Delft - Dynamics of Structures)

Research Group
Mechanics and Physics of Structures
Copyright
© 2022 J. Zou, A. Cicirello, Alexandros Iliopoulos, E. Lourens
DOI related publication
https://doi.org/10.1007/978-3-031-07254-3_29
More Info
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Publication Year
2022
Language
English
Copyright
© 2022 J. Zou, A. Cicirello, Alexandros Iliopoulos, E. Lourens
Research Group
Mechanics and Physics of Structures
Pages (from-to)
290-298
ISBN (print)
978-303107253-6
Reuse Rights

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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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