Variable Structure Control of a Small Ducted Wind Turbine in the Whole Wind Speed Range Using a Luenberger Observer

Abstract

This paper proposes a new variable structure control scheme for a variable-speed, fixed-pitch ducted wind turbine, equipped with an annular, brushless permanent-magnet synchronous generator, considering a back-to-back power converter topology. The purpose of this control scheme is to maximise the aerodynamic power over the entire wind speed range, considering the mechanical safety limits of the ducted wind turbine. The ideal power characteristics are achieved with the design of control laws aimed at performing the maximum power point tracking control in the low wind speeds region, and the constant speed, power, and torque control in the high wind speed region. The designed control laws utilize a Luenberger observer for the estimation of the aerodynamic torque and a shallow neural network for wind speed estimation. The effectiveness of the proposed method was verified through tests in a laboratory setup. Moreover, a comparison with other solutions from the literature allowed us to better evaluate the performances achieved and to highlight the originality of the proposed control scheme.

An MPPT Control of a PMSG-Based WECS with Disturbance Compensation and Wind Speed Estimation

“…where J is the total moment of inertia of the wind turbine coupled with the PMSG and B is the total coefficient of the viscous friction. The one-mass model was also used in [5,6,13,16,20,26,29].…”

“…An input-output feedback linearization with decoupling nonlinear variable transformation is proposed in [15]. Various versions of PI control are discussed in [16][17][18] (a scheme with the aerodynamic torque estimation and compensation), [19] (PI parameters varying with the wind speed), [20] (hybrid LQR-PI control) and [21] (PI with gain scheduling). Local linearization of a WT system model around an operating point is proposed in [22].…”

“…Most commonly used methods are linear and nonlinear disturbance observers (DOBs) with numerical search of a nonlinear function zero (often referred to as the Newton-Raphson search) and estimators based on artificial intelligence techniques. Examples of the first approach are presented in the form of linear transfer function based DOBs in [17,26], Luenberger observers in [16,22], standard Kalman filters in [13,14,18] and adaptive Kalman filtering in [27]. The performance and robustness of the Takagi-Sugeno observer and a linear, extended (EKF) and unscented (UKF) Kalman filter are assessed in [28].…”

“…The performance and robustness of the Takagi-Sugeno observer and a linear, extended (EKF) and unscented (UKF) Kalman filter are assessed in [28]. Effective wind estimators based on artificial intelligence are proposed in [16,29] (sigmoid MLP neural networks), [8] (ANFIS-based estimation) and [11] (support vector machine (SVM) and radial basis function (RBF) neural network with adaptation). An example of a different type of effective wind speed estimation, based on the power balance, is presented in [9].…”

“…Electrical phenomena occurring on the WECS-grid connection, like voltage dips or short circuits, are not considered, so the simulation model does not satisfy some standard requirements [30]. A similar control scheme based on compensation using the aerodynamic torque estimate, but with a different wind speed estimation method, is presented in [16].…”