Research on the Application of Artificial Neural Networks for Voltage Stabilization of Synchronous Generators in Power Systems

Currently, a variety of control methods have been employed to enhance voltage stability performance, including logic control circuits, conventional PID controllers, integrated control systems, and advanced control techniques such as fuzzy control, neural control, and adaptive control. Among these, fuzzy control-based on fuzzy logic theory-has demonstrated significant application potential due to its high adaptability, robustness, good stability, ease of design, and straightforward tuning [1]. Artificial neural networks (ANNs), with their powerful learning and adaptive capabilities, are well-suited to managing the dynamic operating conditions of power systems. By adjusting control parameters in real time, ANNs contribute to optimizing voltage stability [2]. In order to improve control performance, reduce voltage oscillations, and enhance the overall stability of the power system, this study proposes the application of artificial neural networks for voltage stabilization of synchronous generators. This research leverages the notable advantages of neural networks-including self-learning, high adaptability, and efficient handling of nonlinear systems-to contribute to improving power quality [3, 4].