Machine Learning Based LVRT Enhancement of Wind Energy Conversion System using SFCL

Over the previous two decades, the use of renewable energy has expanded substantially. Wind energy conversion is the most developed renewable energy sector and in which DFIG based schemes are widely used. This paper focuses on the enhancement of the LVRT ability of DFIG - WECS by introducing SFCL with machine learning method. In a DFIG-based wind energy conversion system, the stator is directly connected to the grid. The rotor is connected to the grid via two converters: the rotor side converter (RSC) and the grid side converter (GSC). Because renewables’ high penetration produces transient grid disturbances, they must keep running even when grid voltage drops. Low-voltage-ride-through refers to the ability of electric generators to stay connected during transient drops in electric network voltage (LVRT). For DFIG-based wind energy conversion systems, a resistive superconducting fault current limiter is used as a new protection technique. The simulation is done in MATLAB/Simulink and the effectiveness of the super conducting fault current limiter is validated using Machine learning techniques.