PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Optimizing the operation of a photovoltaic generator by a genetically tuned fuzzy controller

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
This paper presents design and application of advanced control scheme which integrates fuzzy logic concepts and genetic algorithms to track the maximum power point in photovoltaic system. The parameters of adopted fuzzy logic controller are optimized using genetic algorithm with innovative tuning procedures. The synthesized genetic algorithm which optimizes fuzzy logic controller is implemented and tested to achieve a precise control of the maximum power point response of the photovoltaic generator. The performance of the adopted control strategy is examined through a series of simulation experiments which prove good tracking properties and fast response to changes of different meteorological conditions such as isolation or temperature.
Rocznik
Strony
145--167
Opis fizyczny
Bibliogr. 15 poz., rys., tab., wzory
Twórcy
autor
  • Faculté d’Electronique et d’Informatique, Université des Sciences et de la Technologie Houari Boumediene (USTHB), B.P. 32, El Alia, 16111 Bab Ezzouar, Algiers, Algeria
autor
  • Faculté d’Electronique et d’Informatique, Université des Sciences et de la Technologie Houari Boumediene (USTHB), B.P. 32, El Alia, 16111 Bab Ezzouar, Algiers, Algeria
autor
  • Ecole Nationale Supérieure d’Informatique, Laboratoire de Communication dans les Systèmes Informatiques B.P 68M, 16309 Oued Smar, El Harrach, Algiers, Algeria
Bibliografia
  • [1] M. Asif and T. Muneer: Energy supply, its demand and security issues for developed and emerging economies. Renewable Sustain Energy Rev., 11(7), (2007), 1388-1413.
  • [2] M. Angel Cid Pastor: Conception et réalisation de modules photovoltaiques électroniques. Thèse de doctorat, Institut National des Sciences Appliquées, Toulouse, France, 2006.
  • [3] P. S. Revankar, W. Z. Gandhare and A. G. Thosar: Maximum power point tracking for PV systems using MATALAB/SIMULINK. In Proc. Second Int. Conf.on Machine Learning and Computing (ICMLC), Bangalore, India, (2010), 8-11.
  • [4] T. Esram and P. L. Chapman: Comparison of photovoltaic array maximum power point tracking techniques. IEEE Trans. on Energy Conversion, 22(2), (2007), 439-449.
  • [5] D. Graham and R. C. Lathrop: The synthesis of optimum transient response: criteria and standard forms. Trans. of the American Institute of Electrical Engineers, Part 2: Applications and Industry, 72 (1953), 273-288.
  • [6] C. Darwin: On the origin of species by means of natural selection or the preservations of favoured races in the struggle of life. John Murray, 1859.
  • [7] J. H. Holland: Adaptation in natural and artificial systems. Ann Arbor, MI, Univ.Michigan Press, 1975.
  • [8] C. Larbes, S. M. Ait Cheikh, T. Obeidi and A. Zerguerres: Genetic algorithms optimized fuzzy logic control for the maximum power point tracking in photovoltaic system. Renewable Energy, 34(10), (2009), 2093-2100.
  • [9] A. Messai, A. Mellit, A. Guessoum and S. A. Kalegirou: Maximum power point tracking using a GA optimized fuzzy logic controller and its FPGA implementation. Solar Energy, 85(2), (2011), 265-277.
  • [10] J. Foran: Optimisation of a fuzzy logic controller using genetic algorithms. Master of Engineering Project Report, School of Electronic Engineering, Dublin City University, 2002.
  • [11] Y. J. Park, H. S. Cho and D. H. Cha: Genetic algorithm-based optimization of fuzzy logic controller using characteristic parameters. In Proc. IEEE Int. Conf. onEvolutionary Computation (ICEC 1995), Perth, WA, Australia, (1995), 831-836.
  • [12] H. Bühler: Réglage par logique floue. Presses Polytechniques et Universitaires Romandes, Lausanne, Switzerland, 1994.
  • [13] P. J. Mac Vicar-Whelan: Fuzzy sets for man machine interactions. Int. J. ofMan Machines Studies, 8(6), (1976), 687-697.
  • [14] D. E. Goldberg: Genetic algorithms in search, optimization and machine learning, Addison Wesley, 1989.
  • [15] N. Khaehintung, K. Pramotung, B. Tuvirat And P. Sirisuk: RISCmicrocontroller built-in fuzzy logic controller of maximum power point tracking for solar-powered light-flasher applications. Dept. of Control & Instrum. Eng, Mahanakorn University of Technology, Bangkok, Thailand, 2004.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-28604395-ef0c-4827-819b-e1c176be6387
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.