Stabilizer Design of PSS3B based on the KH algorithm and Q-Learning for Damping of Low Frequency Oscillations in a Single-Machine Power System

Abstract

Abstract. The aim of this study is to use the reinforcement learning method in order to generate a complementary signal for enhancing the performance of the system stabilizer. The reinforcement learning is one of the important branches of machine learning on the area of artificial intelligence and a general approach for solving the Marcov Decision Process (MDP) problems. In this paper, a reinforcement learning-based control method, named Q-learning, is presented and used to improve the performance of a 3-Band Power System Stabilizer (PSS3B) in a single-machine power system. For this end, we first set the parameters of the 3-band power system stabilizer by optimizing the eigenvalue-based objective function using the new optimization KH algorithm, and then its efficiency is improved using the proposed reinforcement learning algorithm based on the Q-learning method in real time. One of the fundamental features of the proposed reinforcement learning-based stabilizer is its simplicity and independence on the system model and changes in the working points of operation. To evaluate the efficiency of the proposed reinforcement learning-based 3-band power system stabilizer, its results are compared with the conventional power system stabilizer and the 3-band power system stabilizer designed by the use of the KH algorithm under different working points. The simulation results based on the performance indicators show that the power system stabilizer proposed in this study underperform the two other methods in terms of decrease in settling time and damping of low frequency oscillations.