Output Electrical Power Control Of Horizontal Axis Wind Turbine Using Indirect Model Reference Adaptive Neuro Controller

In this paper, we investigated Indirect Model Reference Adaptive Neuro Control (IMRANC), for output electrical power tracking of a nonlinear non-affine Horizontal Axis Wind Turbine (HAWT). Firstly, the nonlinear system is identified by the Nonlinear Autoregressive network with Exogenous inputs (NARX) model that is a recurrent dynamic network. Afterward an IMRANC is designed based on NARX identified model to reach the close loop system in order to get the desired reference model. The MLP networks are applied for both of model and controller subsystems and are then trained by the Marquardt-Levenberg Back-Propagation (LMBP) algorithm while the Tapped Delay Lines (TDL) components are considered over input and feedback paths. Finally, simulation results are presented to validate the effectiveness of the proposed method like robustness and good load disturbance attenuation and accurate tracking, even in the presence of parameter variations due to changing of hydraulic pressure in hydraulic pitch system and also disturbances on the system.