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

Zou, J.; Cicirello, A.; Iliopoulos, Alexandros; Lourens, E.

doi:10.1007/978-3-031-07254-3_29

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

Conference paper (2022)

Authors

J. Zou Offshore Engineering -

A. Cicirello Mechanics and Physics of Structures -

Alexandros Iliopoulos Siemens

E. Lourens Dynamics of Structures - , Offshore Engineering -

Research Group

Mechanics and Physics of Structures () (TU Delft)

DOI: https://doi.org/10.1007/978-3-031-07254-3_29

Bayesian inference Offshore wind turbines Gaussian process Virtual sensing

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Published Date

2022

Language

English

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Faculty

Civil Engineering & Geosciences

Department

Engineering Structures

Research Group

Mechanics and Physics of Structures

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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