A Comprehensive Study of Artificial Neural Networks in Power System Stability

The stability of the power system is important for ensuring the power grid's reliable operation. Traditional stability analysis and stabilization methods have limitations in handling the complexity and nonlinearity of modern power systems. The combination of artificial neural network (ANN)based approaches holds particular promise for power system stability and addressing stabilization challenges. This paper aims to investigate the use of neural methods to improve the analysis and stabilization of complex power systems. Research will focus on neural network architectures and algorithms that can accurately predict and ensure the stability of power systems under various operating conditions. By using neural networks, the research aims to overcome the limitations of traditional methods and provide intelligent solutions to ensure the stability and the robustness of power systems in the face of challenges in the ongoing process. This comprehensive review provides in-depth insights into the applications, techniques, and advancements of ANN in power system stability analysis and control. The study includes ANN basic principles and appropriateness to model complex nonlinear relationships in dynamical systems and evaluate them, including feedforward. This study looks into how ANNs can be used to improve system stability and response to disturbances in areas like fault detection, dynamic modeling, control strategies, and stability assessment.