Modeling Blade-Pitch Actuation Power Use in Wind Turbines

Abstract

Estimating the levelized cost of energy (LCOE) of a wind turbine is useful for performing a cost-benefit analysis of potential designs. The power consumed by blade-pitch actuation is an often neglected, but nontrivial factor in LCOE estimation. The peak power consumption determines the required rating of the actuation motors and the mean power consumption impacts the net annual energy production (nAEP) of the turbine. The closed-loop blade-pitch actuation and the power consumed by its motors are complex functions of the wind field disturbance and internal turbine states. They can only be predicted well with reasonably high-fidelity and computationally expensive simulations or field tests. We present an alternative approach to modeling these signals using the Sparse Identification of Nonlinear Dynamics with Control (SINDyC) methodology. It is computationally tractable to generate these models for large datasets and to efficiently evaluate the required pitching power for a given wind field. Furthermore, the models provide intuition as to how the wind disturbance and blade pitch contribute to the signal dynamics. By generating a closed-form dynamic state equation for the blade-pitch actuation and an algebraic equation for the blade-pitch motor power, we can efficiently predict the mean and maximum power required for a given turbulent wind field and turbine design. The model is trained and validated using data generated from the open-source aero-servo-hydro-elastic wind turbine simulation tool OpenFAST.