AI in Power System Stability Enhancement

This work aims at bringing out a method of applying Artificial Intelligence (AI), more specifically Reinforcement Learning (RL), on power system stability. In more complex power systems with high penetration of renewable sources or in cases with rapidly varying loads, the ability to operate under such conditions becomes a question. RL algorithms in the context of Simulink with MATLAB AI Toolbox provide a stable beginning for real-time cost assessment, choice of control actions, and response to contingencies. Some of the assessed factors include transient stability, frequency fluctuations, and voltage control and system reaction time. As concerning to the performance, it is evidence that learning based control method, which is RLbased approaches, provides better performance than common control strategy like PID and MPC in terms of accuracy, robustness and time. This MATLAB/Simulink environment supported the simulation and verification of RL models and showed how such models can be used to develop the concept of self-healing and smart grids. This research points out the shift in power system stability problems and grid reliability through the application of AI.