Uncertainty quantification of wind turbine fatigue lifetime predictions through binning

Abstract

Aging wind energy assets demand the development of methods able to effectively support informed decision-making. These needs have inspired the use of data-driven methodologies, which offer valuable insights to wind turbine owners and/or operators. Many approaches can be found in the literature for extrapolating fatigue damage measurements to estimate the lifetime of wind turbines. In some cases, resampling approaches are proposed to compute the confidence levels associated with the generated projections, yet a standardized framework has not been adopted. Most reported studies identify the relationship between short-term damage and long-term Environmental and Operational Conditions (EOCs) by mainly rendering mean lifetime predictions and their associated confidence levels, whereas additional predicted lifetime statistical information is usually overlooked. In this work, we showcase the importance of properly accounting for the variability in lifetime predictions, describe how to summarize binned damages using statistical estimators and investigate bootstrapping variants for computing the confidence levels in the generated damage estimators.