TY  - JOUR
AB  - 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 using MATLAB/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.
VL  - 36
IS  - 4
SN  - 0332-1649
DO  - 10.1108/COMPEL-09-2016-0408
UR  - https://doi.org/10.1108/COMPEL-09-2016-0408
AU  - Esmaeili Mehran
AU  - Shayeghi Hossein
AU  - Mohammad Nejad Hamid
AU  - Younesi Abdollah
PY  - 2017
Y1  - 2017/01/01
TI  - Reinforcement learning based PID controller design for LFC in a microgrid
T2  - COMPEL - The international journal for computation and mathematics in electrical and electronic engineering
PB  - Emerald Publishing Limited
SP  - 1287
EP  - 1297
Y2  - 2024/04/19
ER  -