Reinforcement Learning-Based Intelligent Control Algorithm for Flexible DC Low-Voltage Stabilizers

This paper presents an intelligent regulation algorithm for flexible DC low-voltage stabilizers based on reinforcement learning (RL). By incorporating Deep Q-Learning (DQN) into the voltage regulation process, this algorithm enables the stabilizer to adaptively learn optimal control strategies, dynamically adjusting voltage parameters in response to variations in load and grid conditions. The algorithm leverages a self-optimizing mechanism with experience replay and a multi-objective reward function to balance voltage stability and energy efficiency. Experimental results demonstrate that the proposed algorithm enhances response speed, maintains voltage stability, and reduces energy consumption, surpassing traditional PID and fuzzy control methods in complex grid environments.