Global Identification of Wind Turbines Using a Hammerstein Identification Method

In this brief, we present a novel methodology to obtain a nonlinear data-driven model of a wind turbine. We have previously shown that the elementary dynamics of wind turbines can be represented in the form of a multivariable closed-loop Hammerstein structure, where the nonlinear mappings consist of the torque and thrust coefficients. Hammerstein systems consist of a static nonlinearity followed by a linear, time-invariant dynamic subsystem. The dynamic subsystem is identified using a new closed-loop subspace method. The nonlinearity is described using a recently developed regression framework for multivariate splines. We further propose a separable least-squares framework for recovery of the low-rank structure between the nonlinearity and the linear time-invariant system. The method is applied to a detailed simulation of the three-bladed NREL controls advanced research turbine.