BACKSTEPPING INTEGRAL SLIDING MODE CONTROL FOR ENERGY CAPTURE OPTIMIZATION OF WIND TURBINE SYSTEM, 225-234.
Fatima Ez-zahra Lamzouri, El-Mahjoub Boufounas, and Aumeur El Amrani
Keywords
VSWT, backstepping control, ISMC control, GRNN neural network, PSO algorithm
Abstract
This paper reports an efficient nonlinear robust controller design
method of a variable-speed wind turbine, in which maximum wind
energy can be extracted at below the rated wind speed using torque
control. Furthermore, one of the design problems of controllers is
related to the uncertainties in the dynamic model system. To over-
come this problem, a robust controller is investigated; the designed
controller is developed by combining a nonlinear backstepping approach and an integral sliding mode control strategy. The proposed
controller is combined with intelligent systems such as a neural
network and an evolutionary algorithm to improve the controller
performances. However, to predict the uncertain part of the wind
turbine model with lower switching gain, a general regression neural
network (GRNN) is adopted. Thus, a particle swarm optimization
approach with an efficient global search technique is employed by
training online the GRNN weights. In addition, the Lyapunov
approach is proposed to investigate the system stability with the
considered controller. Moreover, a comparison with other strategies
such as backstepping sliding mode control, integral sliding mode
control and sliding mode control controllers is reported. We noticed from the results of simulation that the studied controller presents good performances in terms of transition response and tracking error level.
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