Development of a machine learning model for wind turbine fatigue and ultimate loads based on static loads

Abstract

Abstract A machine learning model is trained on HAWC2 time-domain aeroelastic load simulations in order to provide a load surrogate model from static rotor loads input to lifetime wind turbine design loads, to be utilized in fast design loads evaluation for design optimization. The simulations are performed on the IEA-3.4-130-RWT with a range of design variations for tip-speed-ratio, pitch-ramp settings, and blade length scaling. The surrogate model is shown to provide accurate predictions of lifetime blade and turbine ultimate and fatigue loads for design variations within the training design space.