Reinforcement learning based PID controller design for LFC in a microgrid
| Autor: | Abdollah Younesi, Mehran Esmaeili, H. Shayeghi, Hamid Mohammad Nejad |
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| Rok vydání: | 2017 |
| Předmět: |
Engineering
business.industry Settling time 020209 energy Applied Mathematics 020208 electrical & electronic engineering Automatic frequency control Open-loop controller PID controller Control engineering 02 engineering and technology Computer Science Applications Computational Theory and Mathematics Control theory 0202 electrical engineering electronic engineering information engineering Overshoot (signal) Reinforcement learning Microgrid Electrical and Electronic Engineering business |
| Zdroj: | COMPEL - The international journal for computation and mathematics in electrical and electronic engineering. 36:1287-1297 |
| ISSN: | 0332-1649 |
| DOI: | 10.1108/compel-09-2016-0408 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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