Lifetime reassessment of offshore wind turbines considering different operating conditions using Kriging meta-models

To extend the lifetime of an offshore wind turbine, a lifetime reassessment is required to determine the potential remaining lifetime. This means that the lifetime is recalculated using the combinations of environmental parameters (wind and wave effects) that have actually occurred during the lifetime and which typically differ from the assumed conditions during design. The computational effort of such a lifetime reassessment is significant, as a very large number of aeroelastic simulations have to be carried out. Therefore, according to the state of the art, the number of considered combinations of environmental parameters is reduced similar to the design procedure. In this work, an alternative approach for the lifetime reassessment is investigated in detail. This retains the full set of combinations of environmental parameters while considering both normal operation and idling. The challenge of the high computational effort is resolved by using Kriging meta-models instead of aeroelastic simulations. The meta-model-based approach is compared to two other methods for lifetime reassessment: first, the reference method, i.e. a full lifetime reassessment using aeroelastic simulations where all combinations of environmental parameters which have actually occurred are considered, and second, the approach according to IEC 61400-3. The three methods are compared with regard to the accuracy of their overall lifetime predictions and the computational effort required to make the lifetime predictions. The results show that both the IEC approach and the meta-model approach can achieve good lifetime prediction accuracy compared with the reference method. However, compared to the reference solution, whilst the IEC-based approach can reduce the computational effort to approximately 20 % of the reference solution's computational effort, the use of meta-models can significantly reduce it to less than 0.5 % of the reference solution's computational effort. The results therefore show that, by using meta-models instead of the original aeroelastic simulation model to reassess the lifetime, the computational effort can be significantly reduced compared to the other two methods, while maintaining a high approximation quality in the prediction of the lifetime fatigue loads.