Deep Q-Network based real-time active disturbance rejection controller parameter tuning for multi-area interconnected power systems

Abstract

For the problem of load frequency control (LFC) in multi-area interconnected power systems, a method of linear active disturbance rejection control (LADRC) that uses Deep Q-Network (DQN) to obtain controller parameters in real-time is designed. The main idea of combining the DQN and the LADRC is to equate the state and action in the environment of Reinforcement Learning (RL) to the output of the power system and the parameters of the controller, which will make the controller have learning ability and better adaptability. In order to achieve a better training effect, some improvements have been made to the action selection strategy and reward function. The proposed method is applied to the three-area and four-area interconnected power systems under the influence of disturbances, and the simulation results show the effectiveness of the proposed method. Compared with Proportional-Integral-Derivative (PID) controller and LADRC with fixed parameters, the method proposed in this paper has a better response effect in terms of overshoot and settling time of the power system.