Dynamic modelling and design of various robust sliding mode controls for the wind turbine with estimation of wind speed

Abstract

• Deriving power coefficient of WindPACT 1.5MW wind turbine by FAST and WT-PERF software. • Estimation of wind speed based on ANFIS while training data is obtained by using FAST. • Design and comparison of various sliding mode controls (SMC) to obtain maximum power. • Control of doubly fed induction generator (DFIG) for tracking reference torque. • Robust performance of the high order SMC in the presence of model uncertainties. The main propose of this paper is extracting the maximum efficiency from variable speed wind turbine, which is modelled as an electromechanical system with two masses dynamics. The maximum efficiency can be obtained by tracking the optimal rotor speed, which is controlled by the generator torque as the input. One of the most important information that is required for designing of the control system is the measurement of the effective wind velocity. In this paper, a new ANFIS-based method for estimating the effective wind velocity is developed. The aerodynamic torque has a direct relationship with the power coefficient. So in this paper, power coefficient of WindPACT 1.5 MW turbine as a function of tip speed ratio (TSR) and blade pitch angle is considered. Then, three control methods based on high order sliding mode controllers are examined. The rotor speed and the wind velocity are the only variables required in the design of second and third order sliding mode controllers. FAST (Fatigue, Aerodynamics, Structures and Turbulence) is valid software that offers a fairly complete model of the wind turbine. Results of this paper are validated using FAST. Performance of the designed controllers is compared in terms of the generator torque and desired rotor speed tracking. Finally, the doubly fed induction generator (DFIG) is controlled such that the objectives of reactive power minimization and tracking the desired generator torque are achieved. Two main hindrances in designing the control systems are the uncertainties and the lack of sufficient information on measurements. Therefore robust performance of designed controllers against the model uncertainties is investigated.