Probabilistic temporal extrapolation of fatigue damage of offshore wind turbine substructures based on strain measurements

Abstract

Abstract. Substructures of offshore wind turbines are becoming older and beginning to reach their design lifetimes. Hence, lifetime extensions for offshore wind turbines are becoming not only an interesting research topic but also a relevant option for industry. To make well-founded decisions on possible lifetime extensions, precise fatigue damage predictions are required. In contrast to the design phase, fatigue damage predictions can be based not only on aeroelastic simulations but also on strain measurements. Nonetheless, strain-measurement-based fatigue damage assessments for lifetime extensions have been rarely conducted so far. Simulation-based approaches are much more common, although current standards explicitly recommend the use of measurement-based approaches as well. For measurement-based approaches, the main challenge is that strain data are limited. This means that measurements are only available for a limited period and only at some specific hotspot locations. Hence, spatial and temporal extrapolations are required. Available procedures are not yet standardised and in most cases not validated. This work focusses on extrapolations in time. Several methods for the extrapolation of fatigue damage are assessed. The methods are intended to extrapolate fatigue damage calculated for a limited time period using strain measurement data to a longer time period or another time period, where no such data are available. This could be, for example, a future period, a period prior to the installation of strain gauges or a period after some sensors have failed. The methods are validated using several years of strain measurement data from the German offshore wind farm Alpha Ventus. The performance and user-friendliness of the various methods are compared. It is shown that fatigue damage can be predicted accurately and reliably for periods where no strain data are available. Best results are achieved if wind speed correlations are taken into account by applying a binning approach and if a least some winter months of strain data are available.