Enhancing Power Grid Resilience with Adaptive ANN-Based Voltage Control Strategies

Resilience in power systems is crucial for maintaining stability and ensuring reliable voltage regulation under disturbances and uncertainties. This paper presents an Artificial Neural Network (ANN)-based PID controller for Automatic Voltage Regulation (AVR) that enhances system resilience by improving adaptability and disturbance rejection. Unlike conventional PID controllers, the proposed ANN-based control strategy dynamically adjusts to system variations, making it robust against load changes, parameter fluctuations, and transient disturbances. Simulation results demonstrate that the ANN-based controller significantly reduces overshoot, improves settling time, and enhances steady-state accuracy. The resilience analysis confirms that ANN-based control exhibits superior disturbance rejection and faster recovery compared to traditional PID, ensuring stable voltage regulation in modern power grids. These findings highlight the potential of machine learning-based control strategies in improving the robustness of AVR systems, making them more reliable in dynamic and uncertain environments.