Kanté, B. S., Dansoko, M., Danioko, F., Traore, B. S., Sangaré, M., Ba, A. et Doumbia, M. L. (2024). PV-Wind hybrid system optimization using improved fuzzy logic control. American Journal of Energy Research, 12 (2). pp. 33-39. ISSN 2328-7330 DOI 10.12691/ajer-12-2-1
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Résumé
Abstract
Renewable energy production sources became very attractive in recent years due to environmental problems and their enormous potential. Many studies have been done on these systems alone and combined operation. Among these combined systems, a particular attention is made on the combined PV-Wind system in the literature due to the great sources complementarity and the resource availability in tropical countries as in Mali. Nowadays, PV-Wind system hybridization is an alternative solution to traditional and nuclear energy sources which are the most used to cover the worldwide energy consumption. This system represents an economic and environmental option. Their complementarity improves the service quality compared to PV or wind power single system. A maximum power point tracking controller is required for the electrical power generation profitability. This controller must take into account the sources of intermittent nature and the random variations of climatic parameters. The fuzzy logic controller operates with imprecise input values and can deal with nonlinear equations. This paper presents energy sources modeling and simulation results on fuzzy logic improving (FLI) for hybrid PV-Wind system power optimization. Firstly, a model of each generator is developed and the fuzzy logic rules are defined. Secondly, these models are designed under Matlab/Simulink in order to study their behavior in simulation. Finally, a comparative study is realized between the proposed control and the fuzzy logic controller (FLC) using a fixed gain. The obtained results show the proposed method effectiveness compared to FLC in terms of power optimization and oscillations damping after perturbation.
Type de document: | Article |
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Mots-clés libres: | Hybrid system Renewable energy Simulation Fuzzy logic Optimization |
Date de dépôt: | 21 oct. 2024 12:28 |
Dernière modification: | 21 oct. 2024 12:28 |
Version du document déposé: | Version officielle de l'éditeur |
URI: | https://depot-e.uqtr.ca/id/eprint/11500 |
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