• DocumentCode
    3756416
  • Title

    Comparison of Hill-Climbing and Artificial Neural Network Maximum Power Point Tracking Techniques for Photovoltaic Modules

  • Author

    Zarrad Ons;Jemaa Aymen;Aurelian Craciunescu;Mihai Popescu

  • Author_Institution
    L´ecole Nat. d´Ing. de Monastir, Univ. de Monastir, Monastir, Tunisia
  • fYear
    2015
  • Firstpage
    19
  • Lastpage
    23
  • Abstract
    In this paper, two maximum power point tracking (MPPT) algorithms in a photovoltaic electrical energy generation system are analyzed and compared. The Matlab/Simulink is used to establish the model of a photovoltaic system with MPPT function. This system is developed by combining the models of established solar module and DC-DC boost converter with the algorithms of hill climbing (HC) and artificial neural network (ANC), respectively. The system is simulated under different atmospheric conditions and MPPT algorithms. According to the comparisons among the simulation results, it can be concluded that the photovoltaic system with ANN MPPT algorithm is simpler: it does not require knowledge of internal system parameters, needs less calculation, is faster and provides a compact solution for multi-variable problems.
  • Keywords
    "Artificial neural networks","Maximum power point trackers","Photovoltaic systems","Mathematical model","MATLAB","Neurons"
  • Publisher
    ieee
  • Conference_Titel
    Mathematics and Computers in Sciences and in Industry (MCSI), 2015 Second International Conference on
  • Type

    conf

  • DOI
    10.1109/MCSI.2015.24
  • Filename
    7423936