Combination continuous action reinforcement learning automata & PSO for design of PID controller for AVR system

被引：0

作者：

Hashemi, F. ^{[1
]}

Mohammadi, M. ^{[1
]}

机构：

[1] School of Electrical and Computer Engineering, Shiraz University, Shiraz, Fars, Iran

来源：

International Journal of Engineering, Transactions A: Basics | 2015年 / 28卷 / 01期

关键词：

Analytical systems - Automata - Automatic voltage regulators - Continuous actions - Control performance - Hybrid approach - Proportional integral derivative controllers - Terminal voltages;

D O I：

10.5829/idosi.ije.2015.28.01a.07

中图分类号：

学科分类号：

摘要：

This paper presents a hybrid approach involving Continuous Action Reinforcement Learning Automata (CARLA) and particle swarm optimization (PSO) for design the optimal and intelligent proportional-integral-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 and numerical simulations are provided to verify the effectiveness and feasibility of PID controller of AVR based on CARLA-PSO algorithm.

引用

页码：54 / 61

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