Real-Time Excitation Control-Based Voltage Regulation Using DDPG Considering System Dynamic Performance

Abstract

In recent years, there has been an increasing need for effective voltage control methods in power systems due to the growing complexity and dynamic nature of practical power grid operations. This paper proposes a real-time voltage control method based on deep reinforcement learning (DRL) that continuously regulates the excitation system in response to system disturbances. Dynamic performance is considered during control by incorporating the voltage dynamics data that influence the practical power grid operation. The proposed approach utilizes the deep deterministic policy gradient (DDPG) algorithm, capable of handling continuous action spaces, to adjust the voltage reference of the generator excitation system in real time. To analyze the power system dynamic process, a versatile transmission-level power system dynamic training and simulation platform is developed by integrating the power system simulation software PSS/E and a user-written DRL agent code developed in Python. The platform facilitates the training and testing of various power system algorithms and power grids in dynamic simulations. The efficacy of the proposed method is evaluated based on the developed platform through extensive case studies on the IEEE 9-bus system and the Texas 2000-bus system. The results validate the effectiveness of the approach, highlighting its promising performance in real-time control with respect to dynamic processes.

Keywords

• Voltage control
• deep reinforcement learning
• DDPG
• power system dynamic control
• real-time
• excitation control