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.