Abstract

Power system stability is crucial for ensuring the reliable operation of electrical grids. Instabilities can lead to blackouts, equipment damage, and economic losses. Traditional control methods may struggle to handle the complexity and non-linearity of power systems. This study proposes a novel approach that integrates neuro-fuzzy control with genetic algorithms to enhance power system stability. Neuro-fuzzy systems excel at handling complex and non-linear systems, while genetic algorithms offer efficient optimization capabilities. The neuro-fuzzy control and genetic algorithms provides a robust framework for optimizing power system stability. This approach aims to mitigate the challenges posed by system complexities and uncertainties. Through simulations and case studies, the effectiveness of the proposed method is demonstrated. The integrated approach shows improved stability performance compared to conventional methods. Additionally, the flexibility of the system allows for adaptation to varying operating conditions and disturbances.