| Title |
Design of Fuzzy Logic Controller for Optimal Control of Hybrid Renewable Energy System |
| Authors |
장성대(Seong-Dae Jang) ; 지평식(Pyeong-Shik Ji) |
| DOI |
http://doi.org/10.5370/KIEEP.2018.67.3.143 |
| Keywords |
Hybrid renewable energy system ; Teaching-learning based optimization ; Fuzzy logic controller |
| Abstract |
In this paper, the optimal fuzzy logic controller(FLC) for a hybrid renewable energy system(HRES) is proposed. Generally, hybrid renewable energy systems can consist of wind power, solar power, fuel cells and storage devices. The proposed FLC can effectively control the entire HRES by determining the output power of the fuel cell or the absorption power of the electrolyzer. In general, fuzzy logic controllers can be optimized by classical optimization algorithms such as genetic algorithms(GA) or particle swarm optimization(PSO). However, these FLC have a disadvantage in that their performance varies greatly depending on the control parameters of the optimization algorithms. Therefore, we propose a method to optimize the fuzzy logic controller using the teaching-learning based optimization(TLBO) algorithm which does not have the control parameters of the algorithm. The TLBO algorithm is an optimization algorithm that mimics the knowledge transfer mechanism in a class. To verify the performance of the proposed algorithm, we modeled the hybrid system using Matlab Tool and compare and analyze the performance with other classical optimization algorithms. The simulation results show that the proposed method shows better performance than the other methods. |