Estimating the foundation parameters of offshore wind turbines through Bayesian model updating

Abstract

Abstract The rapid growth of the wind industry has resulted in larger wind turbines with modal properties that lie in the lower frequency range, rendering accurate fatigue assessment increasingly important. However, high uncertainty associated with the support conditions and foundation properties can pose challenges in the condition assessment and fatigue life estimation. One approach to improve these estimates is to use structural monitoring data (e.g. from sensors mounted on the towers) to update the foundation parameters of offshore wind turbine models. However, the low identifiability of the parameters to be estimated can lead to divergent estimates across different frameworks, which, combined with uncertainty in foundation properties, can compromise remaining useful life estimates. In this work, a Bayesian model updating framework is applied to update the foundation parameters of an offshore wind turbine, and its results are compared against a deterministic framework in a numerical example. The advantages of the Bayesian framework over the deterministic framework are discussed in detail and the importance of accurately accounting for uncertainties as part of the model updating process is highlighted.