Iterative Neuro-Fuzzy Hammerstein Model Based Model Predictive Control for Wind Turbines

Abstract

Strong nonlinearity and high volatility are the main features of wind turbine systems. Under the conditions of random wind speed changes, how to quickly control the wind turbine output power within the rated range is a major challenge for wind power system control. This paper proposed an iterative neuro-fuzzy Hammerstein model based model predictive control for wind turbines. Initially, an iterative neuro-fuzzy Hammerstein model is used to characterize the feature of wind turbines. The nonlinear static component's aerodynamic property is estimated using neuro-fuzzy networks, and the linear dynamic component is identified using the AutoRegressive with Exogenous Input (ARX) model. Following this, a generalized wind turbine controlled object with linear properties is constructed by computing the inverse of the nonlinear part, converting the wind turbine's nonlinear control challenge into a linear model control problem. This is done by the unique structure of the Hammerstein model, which allows the linear and nonlinear parts to be separated. Furthermore, a globally convergent parameter learning method is proposed and applied to identify the nonlinear parameters of the Hammerstein model. Eventually, the implementation of the Hammerstein-MPC is compared with the MPC, fuzzy MPC, and PI controllers by FAST simulation. These results demonstrate the superiority of the control effect based on the generalized wind turbine system.