A global focus on the shift to renewable energy has introduced international targets for sustainable energy production, which have strongly increased the interest in offshore wind turbines (OWT’s). In order to make offshore wind energy even more competitive, the levelised cost of
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A global focus on the shift to renewable energy has introduced international targets for sustainable energy production, which have strongly increased the interest in offshore wind turbines (OWT’s). In order to make offshore wind energy even more competitive, the levelised cost of energy for this industry has to be brought down. One of the OWT aspects that can still be developed further, with respect to the cost, is the foundation. As these structures usually have a design lifetime of 25 years and are subjected to cyclic loading, the foundation design is often driven by the fatigue limit state. In order to monitor the fatigue damage imposed on the structure, the stress history at certain “hot spots” needs to be known. Whereas measuring at every hot spot is not feasible, schemes have been developed to estimate the stresses based on a limited amount of sensors.

In this study two distinct approaches for the estimation of operational strains are investigated. The first approach is based on interpolation of section forces which are obtained from strain measurements at different levels; subsequently interpolated section forces can be transformed into strains using basic constitutive relations from structural mechanics. Since the strain measurements have to be taken from the region of interest, i.e. below the mudline, Fibre-Bragg Grating strain sensors are suggested for this application since they are both light and small, thus increasing their chance of survival during pile driving. It is found that the underlying assumption for the Section Force Interpolation (SFI) method, that the moment distribution between two measurement levels can be approximated as varying linearly with the distance to the point of interest, is valid. Therefore, the bending moment of a point in between two measurement levels can be calculated, as well as the related strains.

The second approach is the Multi-Band Modal Decomposition and Expansion (MDE) technique that was introduced by Iliopoulos et al. [1]. This method uses numerically or experimentally obtained structural mode shapes to expand a number of vibrational response measurements into modal responses for a considered frequency band of the measurement data. For OWT’s one quasi-static and two dynamic bands are considered. The total operational strains are obtained by superposition of the strain contributions estimated from each band. Modal strain distributions and strain sensors are used to estimate the strain response in the quasi-static band and a combination of mode shapes, modal strain distributions and accelerometers is used to estimate the strains contribution from the dynamic bands. Furthermore, it is investigated if the Mulit-Band MDE method can be improved to reduce its sensitivity to measurement noise; therefore the Least-Squares (LS) algorithm is replaced by a weighted LS algorithm which can assign weight to measurements according to their relative noise levels.

A number of sensor arrangements and load cases are investigated to find the sensitivity of the estimation accuracy of the Multi-Band MDE method. As the error in the estimation of fatigue consumption should be narrowed down to 2%, it is essential that the optimal sensor arrangement is selected for each operational condition. It has been found that both estimation techniques perform well for uncontaminated input signals, where the MDE outperforms the SFI method. However, if significant levels of white noise are introduced to the measurement signals, the accuracy of the results obtained with the MDE strongly decreases, whereas the SFI technique shows a lower sensitivity to measurement noise.

[1] A. Iliopoulos, W. Woutjens, D. Van Hemelrijk and C. DeVriendt, “Fatigue Assessment of Offshore Wind Turbines on Monopile Foundations using Multi-Band Modal Expansion,” Wind Energy, February 2017.