Comparison of Fuzzy and Neural Networks Controller for MPPT of Photovoltaic Modules

  • Aouatif Ibnelouad
  • Abdeljalil El Kari
  • Hassan Ayad
  • Mostafa Mjahed
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 37)


The present paper proposes a comparison between two control methods for maximum power point tracking (MPPT) of a photovoltaic (PV) system under varying irradiation and temperature conditions: the fuzzy control method and the neural networks control method. The results of simulation obtained have been developed and analyzed by using Matlab/Simulink software for the both techniques have. The power transitions at varying irradiation and temperature conditions are observed and the power tracking time appreciated by the neural networks controller against the fuzzy logic controller has been evaluated.


MPPT Photovoltaic module Neural networks controller Fuzzy logic controller Matlab/Simulink models 


  1. 1.
    Lalouni, S., Rekioua, D., Rekioua, T., Matagne, E.: Fuzzy logic control of stand-alone photovoltaic system with battery storage. Power Sources 193, 899–907 (2009)CrossRefGoogle Scholar
  2. 2.
    Glasner, I.: Advantage of boost vs. buck topology for maximum power point tracker in photovoltaic systems. TelAviv University, Faculty of Engineering, Department of Electrical Engineering, Israel, pp. 355–358. IEEE (1996)Google Scholar
  3. 3.
    Mujadi, E.: ANN based peak power tracking for PV supplied DC motors. Solar Energy 69(4), 343–354 (2000)CrossRefGoogle Scholar
  4. 4.
    Mujadi, E.: PV water pumping with a peck-power tracker using a simple six step square-wave inverter. IEEE Trans. Ind. Appl. 33(3), 714–721 (1997)CrossRefGoogle Scholar
  5. 5.
    Bose, B.K.: Microcomputer control of a residential photovoltaic power conditioning system. IEEE Trans. Ind. Appl. IA-21(5), 1182–1191 (1985)CrossRefGoogle Scholar
  6. 6.
    Villalva, M.G., Gazoli, J.R., Filho, E.R.: Comprehensive approach to modeling and simulation of photovoltaic arrays. IEEE Trans. Power Electron. 24(5), 1198–1208 (2009)CrossRefGoogle Scholar
  7. 7.
    RezaReisi, A., Moradi, M.H., Jamasb, S.: Classification and comparison of maximum power point tracking techniques for photovoltaic system: a review. Renew. Sustain. Energy Rev. 19, 433–443 (2013)CrossRefGoogle Scholar
  8. 8.
    Ishaque, K., Abdullah, S.S., Ayob, S.M., Salam, Z.: Single input fuzzy logic controller for unmanned underwater vehicle. J. Intell. Robot. Syst. 59, 87–100 (2010)CrossRefzbMATHGoogle Scholar
  9. 9.
    Chian-Song, C.: T-S fuzzy maximum power point tracking control of solar power generation systems. IEEE Trans. Energy Convers. 25, 1123–1132 (2010)CrossRefGoogle Scholar
  10. 10.
    Hatti, M.: Contrôleur flou pour la poursuite du point de puissance maximum d’un système photovoltaique, Huitieme Conference des Jeunes Chercheurs en Genie Electrique (JCGE 2008), Lyon (2008)Google Scholar
  11. 11.
    Aymen, J., Ons, Z., Craciunescu, A., et al.: Comparison of fuzzy and neuro-fuzzy controllers for maximum power point tracking of photovoltaic modules. In: Proceeding of IEEE International Conference on Renewable Energies and Power Quality (ICREPQ-2016) (2016). ISSN 2172-038Google Scholar
  12. 12.
    Tahar, R.: Application de l’intelligence artificielle au problème de la stabilité transitoire des réseaux électriques, Thèse magister (2005)Google Scholar
  13. 13.
    Parizeau, M.: Réseaux de neurones, Livre PDF (2004)Google Scholar
  14. 14.
    Harendi, A.: Modélisation et simulation d’un système photovoltaïque, UNIVERSITE KASDI MERBAH OUARGLA, Faculté des Sciences Appliquées, Département de Génie Electrique (2014)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Aouatif Ibnelouad
    • 1
  • Abdeljalil El Kari
    • 1
  • Hassan Ayad
    • 1
  • Mostafa Mjahed
    • 2
  1. 1.Faculty of Sciences and TechnologiesMarrakechMorocco
  2. 2.Department of Mathematics and SystemsRoyal School of AeronauticsMarrakechMorocco

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