An emotional control approach to grid-connected DFIG based wind turbine

This paper presents an innovative controller inspired by human emotional intelligence and learning mechanisms called Brain Emotional Learning-Based Intelligent Controller (BELBIC), introduced and applied to enhance the operation of a wind energy conversion system based on Doubly Fed Induction Generator (DFIG). The BELBIC control approach combines emotional states and reinforcement learning concepts to optimize control actions effectively. This paper focuses on BELBIC control of the rotor side converter and grid side converter of the DFIG system. The controller's performance is systematically evaluated under various scenarios, such as constant wind speed, variable wind speed, step change in reactive power, and voltage sag, and the performance of the above scheme is compared with that of the classical PI controller. The simulation results of the DFIG system, employing a BELBIC-based control strategy, show excellent dynamic performance in regulating the output powers of the doubly fed induction generator in grid-connected mode, thereby validating the effectiveness of the proposed control approach.