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

Area of Science:

• Electrical Engineering
• Renewable Energy Systems
• Control Theory

Background:

• Doubly Fed Induction Generators (DFIGs) are crucial for wind energy integration but require robust Low-Voltage Ride-Through (LVRT) capabilities for grid stability.
• Traditional control schemes often exhibit inadequate performance during grid disturbances, necessitating advanced control strategies.

Purpose of the Study:

• To develop and validate a novel Neural Inverse Optimal Control (N-IOC) scheme to significantly enhance the LVRT capacity of DFIGs.
• To improve the stability and reliability of power systems with high wind energy penetration.

Main Methods:

• A recurrent high-order neural network was employed to model the DFIG and DC-link dynamics.
• The Neural Inverse Optimal Control (N-IOC) strategy was synthesized based on the developed dynamic model.
• The proposed controller was experimentally validated on a 1/4 HP DFIG prototype under various grid disturbance scenarios.

Main Results:

• The N-IOC scheme demonstrated effective enhancement of LVRT capacity in DFIGs.
• The controller operated effectively without the need for decomposition processes or additional hardware.
• Experimental validation confirmed the controller's performance under diverse grid fault conditions.

Conclusions:

• The proposed N-IOC is a highly effective solution for improving DFIG LVRT capabilities.
• This control strategy contributes to enhanced power system stability with increased wind energy integration.
• The N-IOC offers a practical and efficient approach for LVRT enhancement without complex system modifications.