Intelligent robust pitch control of wind turbine using brain emotional learning

This article proposes an implementation of the brain emotional learning-based intelligent controller (BELBIC) for high-precision and robust pitch control of a 5-MW wind turbine. The proposed model-free controller is a biologically inspired method emulating the learning in the mammalian's limbic system and it is independent of the model dynamics and variations that might occur in a system. The auto-learning capability of the BELBIC allows accommodating the nonlinearities associated with the wind turbine model and provides a reasonable degree of disturbance enabling precise and robust tracking of the pitch angle, even under unforeseen wind conditions. To investigate the trajectory tracking performance and robustness of the BELBIC in various unpredictable wind conditions, multiple uncertain wind speed conditions including gust and random wind, are simulated in MATLAB/Simulink. The results of simulations are compared with two benchmark control methods, fuzzy-proportional-integral-derivative and gain-scheduling proportional-integral. The simulation results clearly indicate that the BELBIC serves better performance and robustness while guaranteeing quick and precise pitch angle response as well as its ability in dealing with nonlinearity and unforeseen wind conditions in comparison to the other two benchmark control methods.