  Dynamic fluctuations in the wind field to which a wind turbine (WT) is
exposed to are responsible for fatigue loads on its components. To reduce
structural loads in WTs, advanced control schemes have been proposed. In recent
years, prognosis-based lifetime control of WTs has become increasingly
important. In this approach, the prognostic controller gains are adapted based
on the stateof-health (SOH) of the WT component to achieve the desired
lifetime. However, stochastic wind dynamics complicates estimation of the SOH
of a WT. More recently, robust controllers have been combined with real-time
damage evaluation models to meet prognosis objectives. Most rely on model-based
online load cycle counting algorithms to determine fatigue damage, with
analytical models providing the degradation estimate. However, most use load
measurements that are either unreliable or unavailable in commercial WTs,
limiting their practicality. In this contribution, a hybrid prognosis scheme
combining data-driven load prediction and model-based damage estimation models
for robust lifetime control of commercial WTs is proposed. A data-driven
support vector machine (SVM) regression model is trained using loading data
obtained from dynamic simulations using a {\mu}-synthesis robust disturbance
accommodating controller (RDAC). The regression model uses available WT
measurements to predict tower load. Based on this prediction, an online
rain-flow counting (RFC) damage evaluation model estimates the damage level and
lifetime of the tower. The RDAC controller gains are dynamically adapted to
achieve a predefined damage limit and lifetime. The proposed approach is
evaluated on a 5 MW reference WT and its performance is compared with a
model-based prognosis scheme using ideal WT tower measurement. Results
demonstrate the efficacy of the proposed approach to control the fatigue
lifetime in WT components.
