Variable speed wind turbine power control: A comparison between multiple MPPT based methods

Abstract

Reducing the renewable energy costs is necessary for the competition with the fossil energies and control strategies have great impact on the efficiency of wind machines. In the wind turbine industry, a practical approach is to maximize the energy capture of a wind machine by optimizing the power coefficient in the under-rated situations. In this paper, with the main objective of maximizing the energy capture in the second region, four different control strategies are compared in the presence of uncertainties. The proposed control methods are compared based on their power capture and robustness against probable uncertainties in the structural and environmental parameters. A two-mass mechanical model is used and verified by FAST simulations. The generator torque is considered as the control input and the control objective is to track the rotor angular velocity in order to achieve the optimum value of power coefficient. First, a common sliding mode controller is reviewed, then a modern PI-Neural Network controller (PI-NN) studied, and then a backstepping controller is designed; finally, a hybrid H∞ and feedback linearization controller (H∞-FL) is introduced. While all controllers demonstrate an appropriate performance in terms of the tracking and efficiency indices, backstepping method shows better efficiency, Sliding mode and PI-NN are similar in terms of power efficiency and H∞-FL controller showed the least efficiency. On the other hand, the backstepping controller suffer from the high fluctuations in control signal and H∞-FL controller has a considerably smoother torque. The proposed controllers can also come up with the disturbances so that they can tolerate the structural and environmental parameters discrepancy to some extent. The backstepping and H∞-FL controllers showed similarly the best robustness, but sliding mode controller shows less robustness. The robustness of the PI-NN performance lies between the sliding mode and backstepping controller.