Control-Oriented Low-Order Approximation and Reconstruction of Yaw-Excited Wind Turbine Wake Dynamics

Abstract

The estimation and reconstruction of complex fluids like wind turbine wake are challenging problems, which require approximating high-dimensional, nonlinear dynamic systems with a limited number of physical measurements. Current works mainly focus on the reduced-order approximation for fluid dynamics like dynamic mode decomposition, or linear stochastic estimation for static mapping. In this article, the problem of yaw-controlled wind turbine wake dynamics approximation and reconstruction are addressed. A Koopman-linear flow estimator is designed, which forms a control-oriented linear dynamic state-space model. The full wake flow is first approximated on its dynamic performances and then reconstructed from low-order physical states. The novelty mainly yields on the added yaw excitation that a control-oriented dynamic model is obtained. The Kalman filter is also involved that measured data in future time could be embedded while the measurement noise is filtered. At last, static and dynamic estimation tests verify the effectiveness of the proposed estimator.