Effective wind speed estimation based on a data-driven model of wind turbine tower deflection

Abstract

The deflection of the wind turbine tower can provide us with rich information about the effective wind speed. In this paper, a new method for effective wind speed estimation is proposed based on tower deflection. The tower vibration model is derived and a subspace identification method is used to identify the model parameters. An online estimator of aerodynamic thrust force based on the identified tower model is designed and then implemented using a Kalman filter together with a recursive least squares algorithm. The estimated aerodynamic thrust force is then used as an input to a neural network estimator, which is trained to invert the aerodynamic thrust force equation and estimate the effective wind speed. In order to show the performance of the proposed estimator, the estimated thrust force and wind speed are compared and verified with a third-party simulation data of a 1.5 MW wind turbine. The comparison shows close agreement between their values.