An Intelligent Coordinated Control System for Power Transformers Using Deep Q-Network

Automatic coordinated control of power transformers is essential to stable operation of power systems. However, there still lack mature intelligent solutions for this purpose. As a result, this paper proposes a reinforcement learning-based automatic collaborative control approach for power transformers. Firstly, by establishing the state space and action of the transformer control system, deep reinforcement learning is used to optimize the control strategy. Then, deep Q-network is utilized to automatically learning and adjust the operating parameters of each transformer to achieve coordinated control of power systems. In the algorithm design, we consider multiple factors such as power grid load and voltage stability. And they are incorporated into the reward function of reinforcement learning to facilitate learning appropriate control strategies. The experimental results show that the new method not only improves the response speed of the control system, but also effectively enhances the stability and robustness. In addition, we conducted in-depth analysis on the convergence and learning efficiency of the system, verifying the feasibility and superiority of the proposed method compared with traditional methods.