Probabilistic Modeling of Wind Turbine Drivetrain Components

Wind energy is one of several energy sources in the world and a rapidly growing industry in the energy sector. When placed in offshore or onshore locations, wind turbines are exposed to wave excitations, highly dynamic wind loads and/or the wakes from other wind turbines. Therefore, most components in a wind turbine experience highly dynamic and time-varying loads. These components may fail due to wear or fatigue, and this can lead to unplanned shutdown repairs that are very costly. The design by deterministic methods using safety factors is generally unable to account for the many uncertainties. Thus, a reliability assessment should be based on probabilistic methods where stochastic modeling of failures is performed. This thesis focuses on probabilistic models and the stochastic modeling of the fatigue life of the wind turbine drivetrain.
Hence, two approaches are considered for stochastic modeling of the fatigue life. One method is based on the classical Weibull approach and the other on application of a log-normal distribution. The statistical parameters in both models are estimated and applied in reliability assessments. Furthermore, the thesis includes a study of the effect of defects/nodules on fatigue life of cast iron samples. The cast iron samples scanned by 3D tomography equipment at the DTU Wind Energy (Risø campus), and the distribution of nodules are used to estimate the fatigue life.