Neural Network Predictive Control for Improved Reliability of Grid-Tied DFIG-Based Wind Energy System under the Three-Phase Fault Condition

This research explores a distinctive control methodology based on using an artificial neural predictive control network to augment the electrical power quality of the injection from a wind-driven turbine energy system, engaging a Doubly Fed Induction Generator (DFIG) into the grid. Because of this, the article focuses primarily on the grid-integrated wind turbine generation's dependability and capacity to withstand disruptions brought on by three-phase circuit grid failures without disconnecting from the grid. The loading of the grid-integrated power inverter causes torque and power ripples in the DFIG, which feeds poor power quality into the power system. Additionally, the DC bus connection of the DFIG's back-to-back converters transmits these ripples, which causes heat loss and distortion of the DFIG's phase current. The authors developed a torque and power content ripple suppression mechanism based on an NNPC to improve the performance of a wind-driven turbine system under uncertainty. Through the DC bus linkage, it prevented ripples from being transmitted. The collected results are evaluated and compared to the existing control system to show the advancement made by the suggested control approach. The efficacy of the recommended control methodology for the under-investigation DFIG system is demonstrated through modelling and simulation using the MATLAB Simulink tool. The most effective control technique employed in this study's simulations to check the accuracy of the suggested control methodology was the NNPC.