Real-time Neural Inverse Optimal Control for Low-Voltage Rid-Through enhancement of Double Fed Induction Generator based Wind Turbines

Abstract

The Low-Voltage Ride-Through (LVRT) capacity of the Doubly Fed Induction Generator (DFIG) is one of the important requirements to ensure power systems stability, incorporating wind energy. While traditional control schemes present inappropriate performances under disturbances, this paper introduces a novel Neural Inverse Optimal Control (N-IOC) scheme for LVRT capacity enhancing. The developed controller is synthesized using recurrent high order neural network, which is utilized to build-up the DFIG and the DC-link dynamics. Based on such identifier, the proposed N-IOC is synthesized. This controller is experimentally validated on 1∕4 HP DFIG prototype considering various grid disturbances. Results illustrate the proposed controller effectiveness for LVRT enhancement without required decomposition process and/or any additional device.