Design of a Robust Adaptive Controller for the Pitch and Torque Control of Wind Turbines

Abstract

In this paper, robust adaptive control is designed for pitch and torque control of the wind turbines operating under turbulent wind conditions. The dynamics of the wind turbine are formulated by considering the five degrees of freedom system (rotor angle, gearbox angle, generator angle, flap-wise deflection of the rotor blade, and axial displacement of the nacelle). The controller is designed to maintain the rotor speed, maximize the aerodynamic efficiency of the wind turbine, and reduce the loads due to high wind speeds. Gaussian probability distribution function is used for approximating the wind speed, which is given as the disturbance input to the plant. The adaptive control algorithm is implemented to 2 MW and 5 MW wind turbines to test the robustness of the controller for varying parameters. The simulation is carried out using MATLAB/Simulink for three cases, namely pitch control, torque control, and the combined case. A case study is done to validate the proposed adaptive control using real wind speed data. In all the cases, the results indicate that the rotor speed follows the reference speed and show that the designed controller gives a satisfactory performance under varying operating conditions and parameter variations.

Grid Frequency and Amplitude Control Using DFIG Wind Turbines in a Smart Grid

“…Equations ( 8) and (9) shown that the stator current is controllable with the rotor current. With the omission of the stator resistance because it is so small, the stator flux can be assumed to be constant and its value is,…”

“…DFIG generators provide access to the rotor windings and regulating the rotor voltage, the generator active and reactive powers are fully controllable. Therefore, the design and implementation of a new control scheme for a DFIG based wind turbine system has attracted the attention of several authors in the last years [8][9][10][11][12].…”

Exploring Reward Strategies for Wind Turbine Pitch Control by Reinforcement Learning

“…Learning and forgetting with learning rate (LF-LR). The previous methods do not forget any previous reward; this may be effective for steady systems but for changing models it might be advantageous to forget some previous rewards, Equation (13). The forgetting factor is modelled as the complementary leaning rate (1 − α).…”

“…In Yin et al [12], an adaptive robust integral SMC pitch angle controller and a projection-type adaptation law are synthesized to accurately track the desired pitch angle trajectory, while it compensates model uncertainties and disturbances. Bashetty et al propose an adaptive controller for pitch and torque control of the wind turbines operating under turbulent wind conditions [13].…”

“…Figure13. Evolution of the MSE (left) and variance (right) for different reward sizes when velocity reward strategy (VRS) is applied.…”