• DocumentCode
    2675107
  • Title

    Maximum power point tracking using GA-optimized artificial neural network for Solar PV system

  • Author

    Ramaprabha, R. ; Gothandaraman, V. ; Kanimozhi, K. ; Divya, R. ; Mathur, B.L.

  • Author_Institution
    Dept. of Electr. & Electron. Eng., SSN Coll. of Eng., Chennai, India
  • fYear
    2011
  • fDate
    3-5 Jan. 2011
  • Firstpage
    264
  • Lastpage
    268
  • Abstract
    Solar energy is a green energy which is not only perennial but also accessible to every strata of the world. An easy way to convert solar energy into electric energy is to use Solar Photovoltaic (SPV) system. Solar panel is a power source having nonlinear internal resistance. As the intensity of light falling on the panel varies, its voltage as well as its internal resistance varies. To extract maximum power from the panel, the load resistance should be equal to the internal resistance of the panel. For this purpose maximum power point trackers (MPPT) are used. This paper proposes a new MPPT controller. The proposed MPPT controller is based on genetic algorithm (GA) optimized artificial neural network (ANN). For the simulation purpose an improved model of SPV is used. The MPPT is simulated and studied using MATLAB software.
  • Keywords
    genetic algorithms; maximum power point trackers; neural nets; photovoltaic power systems; power engineering computing; solar cells; MATLAB software; MPPT controller; artificial neural network; electric energy; genetic algorithm; green energy; maximum power point tracking; nonlinear internal resistance; solar energy; solar panel; solar photovoltaic system; Artificial neural networks; Biological system modeling; Gallium; Integrated circuit modeling; Mathematical model; Photovoltaic systems; Resistance; ANN; GA; MATLAB; MPPT; Solar PV system;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Electrical Energy Systems (ICEES), 2011 1st International Conference on
  • Conference_Location
    Newport Beach, CA
  • Print_ISBN
    978-1-4244-9732-4
  • Type

    conf

  • DOI
    10.1109/ICEES.2011.5725340
  • Filename
    5725340