• DocumentCode
    2670199
  • Title

    Intelligent control in photovoltaic systems by neural network

  • Author

    Dkhichi, Fayrouz ; Oukarfi, Benyounes

  • Author_Institution
    Electr. Eng. Dept., Hassan II Casablanca Univ., Mohammedia, Morocco
  • fYear
    2015
  • fDate
    25-26 March 2015
  • Firstpage
    1
  • Lastpage
    5
  • Abstract
    The Artificial Neural Network (ANN) method studied in this paper is assigned as an intelligent control of photovoltaic (PV) system. The objective of this control is to make the load operate at the maximum electrical power generated by the PV module. In this aim, the ANN consists to track the optimal duty cycle of the electronic converter, in order to lead to the Maximum Power Point (MPP) of the PV system. Moreover, the two classical methods: Perturb and Observe (P&O) and Incremental Conductance (IncCon) are studied in the sake of comparison with the ANN method, by taking into consideration the efficiency, the speed and the robustness performance when the meteorological conditions change.
  • Keywords
    maximum power point trackers; neurocontrollers; photovoltaic power systems; electronic converter; intelligent control; maximum electrical power generation; maximum power point; neural network; photovoltaic systems; Artificial neural networks; Intelligent control; Mathematical model; Maximum power point trackers; Oscillators; Software packages; Steady-state; Artificial Neural Network; Intelligent Control; Maximum Power Point Tracker; PV system;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Systems and Computer Vision (ISCV), 2015
  • Conference_Location
    Fez
  • Print_ISBN
    978-1-4799-7510-5
  • Type

    conf

  • DOI
    10.1109/ISACV.2015.7106181
  • Filename
    7106181