Wind turbine gust estimation using remote sensing data

The offshore wind energy industry is experiencing sustained growth as governments around the world look to secure low-carbon sources of energy. In order to capture more energy from the wind, larger turbines are being designed, leading to the structures becoming increasingly vulnerable to damage caused by violent gusts of wind. Advance knowledge of such gusts will enable turbine control systems to take preventative action, reducing the cost of turbine maintenance. Therefore, in this paper we present a methodology to estimate the velocity profile of an oncoming wind field, given only limited spatio-temporal measurements from typical light detection and ranging (LiDAR) instruments, mounted on the turbine nacelle. The main contribution of this paper lies in the derivation of a simplified deterministic model of atmospheric boundary-layer flows, based on the Navier-Stokes equations, that enables subsequent implementation of an unscented Kalman Filter. Results are presented that compare the accuracy of the estimated wind field to actual wind-data produced from large eddy simulations of the atmospheric boundary layer.