Enhancing Rotor Angle Stability of Synchronous Generators Using Neuro-Fuzzy Excitation Control Model

Abstract

This paper proposes a neuro-fuzzy excitation control model to enhance the rotor angle stability of synchronous generators. The proposed model combines the advantages of both neural networks and fuzzy logic control to improve the performance of the excitation system. The proposed model is designed to regulate the excitation system to generate the required reactive power and maintain the synchronous operation of the generator. The proposed model is tested on a single-machine infinite-bus power system, and the results are compared with a conventional proportional-integral (PI) controller. The simulation results demonstrate that the neuro-fuzzy excitation control model provides better performance than the PI controller in terms of transient stability, damping oscillations, and response to disturbances. The proposed model also shows robustness against changes in system parameters and different operating conditions. The results of this study suggest that the neuro-fuzzy excitation control model can be a suitable alternative to conventional PI controllers in enhancing the rotor angle stability of synchronous generators.