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

Abstract

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.

A Three-Level Neutral-Point-Clamped Converter Based Standalone Wind Energy Conversion System Controlled with a New Simplified Line-to-Line Space Vector Modulation

“…Several recent pieces of research on DFIG-based WECS control techniques can be distinguished [24][25][26][27]. In a standalone WECS mode, the frequency and magnitude of the DFIG stator output voltage have to be controlled and should be fixed at nominal values, regardless of the variable rotor speed [28].…”

Fractional-order neural control of a DFIG supplied by a two-level PWM inverter for dual-rotor wind turbine system

“…The simulation results showed the superiority and high efficiency of the Hopfield fuzzy NN in improving the dynamic performance of the studied system compared to the traditional control. In Behara and Saha, 52 artificial neural predictive control was used to improve the quality of energy generated by DFIG in different working conditions. This strategy aims to enhance the productivity of wind turbines, achieve a stable network connection, and ensure that they operate as efficiently as possible.…”