Radial Basis Function Artificial Neural Network Optimized Stability Analysis in Modified Mathematical Modeled Type-III Wind Turbine System Using Bode Plot and Nyquist Plot

Abstract

Stability is the major factor that should be maintained in every power system. To predict and to optimize the nonlinear parameters this research provides a control system transient analysis using Bode plot and Nyquist plot to regulate the stability in wind power generation. The rotor speed should be balanced with respect to the generation of generator power for viable wind power generation. This study introduces a sliding mode controller for controlling wind speed and preserving system stability, and it may be improved using an Artificial Neural Network based Radial Basis Function Neural Network to eliminate nonlinearities induced by changing wind speed. The tip speed ratio approach is utilized in this study to harvest the most power from wind energy. To optimize this TSR method, a PI-RBFN tuned sliding mode controller was utilized to get maximum power while minimizing active power losses. This proposed approach may be used to address nonlinearities in the pitch angle caused by changing wind speed. As a result, the resilience of the redesigned Type-III wind turbine system is investigated using MATLAB simulink in this study. The simulation results are compared to the current DFIG-based modified Type-III wind turbine method.