Combination of Continuous Action Reinforcement Learning Automata and PSO to Design a PID Controller for AVR System

This paper presents a hybrid approach involving Continuous Action Reinforcement Learning Automata (CARLA) and particle swarm optimization (PSO) to design a optimal and intelligent proportionalintegral- derivative (PID) controller of an automatic voltage regulator (AVR) system. The proposed method is CARLA which is able to explore and learn to improve control performance without the knowledge of the analytical system model. The role of an AVR is to hold the terminal voltage magnitude of a synchronous generator at a specified level. Hence, the stability of the AVR system would seriously affect the security of the power system. CARLA-PSO is a method that combines the features of PSO and CARLA in order to improve the optimize operation. The proposed method was indeed more efficient and robust in improving the step response of an AVR system. Numerical simulations are also provided to verify the effectiveness and feasibility of PID controller of AVR based on CARLA-PSO algorithm.