Enhanced grid stability of DFIG-based wind systems through intelligent reactive power coordination using machine learning-based control.

This research aims to improve grid stability in wind farms that use Doubly Fed Induction Generators (DFIGs). Adding large amounts of wind power to modern grids can cause voltage swings and make the system less stable during transients. To tackle these problems, the project looks at smart ways to coordinate reactive power. It uses machine‑learning control algorithms that adjust the reactive power from both the rotor‑side converter and the grid‑side converter whenever the grid is disturbed. The approach starts by modeling a DFIG turbine under different grid events, such as voltage sags and swells. A machine‑learning model is then trained to predict the best reactive‑power set points in real time.

Support for implementation and simulation is provided to test: * transient response, * voltage recovery time, * overall compliance with grid standards. The new control scheme is intended to give strong voltage support, reduce active‑power oscillations, and improve low‑voltage ride‑through capability. It also lets researchers compare the performance of intelligent controllers with traditional PI‑based coordination methods under dynamic grid conditions.

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