Reinforcement learning based PID controller design for LFC in a microgrid

被引:18
|
作者
Esmaeili, Mehran [1 ]
Shayeghi, Hossein [1 ]
Nejad, Hamid Mohammad [1 ]
Younesi, Abdollah [1 ]
机构
[1] Univ Mohaghegh Ardabili, Tech Engn Dept, Ardebil, Iran
关键词
Design optimization; Control theory; Adaptive fuzzy logic control; Electrical power systems; Microgrid; Reinforcement learning; FREQUENCY CONTROL; ENERGY; SYSTEM;
D O I
10.1108/COMPEL-09-2016-0408
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Purpose - This paper aims to propose an improved reinforcement learning-based fuzzy-PID controller for load frequency control (LFC) of an island microgrid. Design/methodology/approach - To evaluate the performance of the proposed controller, three different types of controllers including optimal proportional-integral-derivative (PID) controller, optimal fuzzy PID controller and the proposed reinforcement learning-based fuzzy-PID controller are compared. Optimal PID controller and classic fuzzy-PID controller parameters are tuned using Non-dominated Sorting Genetic Algorithm-II algorithm to minimize overshoot, settling time and integral square error over a wide range of load variations. The simulations are carried out usingMATLAB/SIMULINK package. Findings - Simulation results indicated the superiority of the proposed reinforcement learning-based controller over fuzzy-PID and optimal-PID controllers in the same operational conditions. Originality/value - In this paper, an improved reinforcement learning-based fuzzy-PID controller is proposed for LFC of an island microgrid. The main advantage of the reinforcement learning-based controllers is their hardiness behavior along with uncertainties and parameters variations. Also, they do not need any knowledge about the system under control; thus, they can control any large system with high nonlinearities.
引用
收藏
页码:1287 / 1297
页数:11
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