Optimized PI Controller for DFIG Grid Integration Using Neural Tuning and Dense Plexus Terminals

The integration of Doubly-Fed Induction Generators (DFIG) into electrical grids poses significant challenges due to the complex dynamics involved. This paper presents a novel approach to optimize the Proportional-Integral (PI) controller for DFIG grid integration by employing neural network-based tuning com-bined with Dense Plexus Terminals (DPT). The proposed method leverages the adaptive learning capabilities of neural networks to fine-tune the PI controller parameters in real-time, enhancing the system's responsiveness to dynamic grid conditions. Dense Plexus Terminals further improve the robustness and efficiency of the control strategy by facilitating enhanced signal processing and reducing computational load. Simulation results demonstrate that the optimized PI controller achieves superior performance in terms of stability, transient response, and power quality compared to conventional tuning methods. The findings suggest that the integration of neural tuning and Dense Plexus Terminals presents a promising direction for improving the control of DFIGs in modern power grids, ensuring reliable and efficient energy conversion under varying operational conditions.