DFIG-based wind turbine using DVR with neural network control for augmentation of Fault ride through

Abstract

The aim of this paper is to present a series grid interface topology for enhancing the fault ride-through (FRT) efficiency of DFIG-based wind turbines. The most commonly used type of generator, the doubly fed induction generator-based wind turbine (DFIG-WT), is highly susceptible to grid voltage disturbances. The topology depicts a WT that is connected to an electric grid and uses DVR. DVR is a series interface method coupled to DFIG-WT, to resolve the voltage issue and conform with WT's FRT capability. Since DVR can provide quick voltage sag mitigation during faults, help ride through capability, and boost grid power quality with integrated wind generators, it is a good fit for grid operations. The control algorithm used defines the solution’s effectiveness in overcoming most grid faults. The use of combined feed forward and feedback control based on neural network control to increase DVR performance is investigated in this research in terms of DC connection voltage balancing, active and reactive power assistance, mitigation of voltage sag and for DFIG WT’s FRT capability when there is a balanced sag and short circuit fault condition. The DVR’s operation, with the ability of this control to compensate for sag and regulate load voltage to the best of its ability is confirmed by findings from a 1.5 MW grid- connected DFIG WT MATLAB simulation.