• DocumentCode
    3591519
  • Title

    Performance analysis of neural network and fuzzy logic based MPPT techniques for solar PV systems

  • Author

    Gupta, Ankit ; Kumar, Pawan ; Pachauri, Rupendra Kumar ; Chauhan, Yogesh K.

  • Author_Institution
    Sch. of Eng., Electr. Eng. Dept., Gautam Buddha Univ., Noida, India
  • fYear
    2014
  • Firstpage
    1
  • Lastpage
    6
  • Abstract
    The maximum power point tracking (MPPT) technique in the photovoltaic (PV) system is used to achieve maximum power through the solar PV system. Therefore, the interest is generated to design a more effective and efficient MPPT to achieve maximum power transfer to the load. In this context, two MPPT techniques, i.e. artificial neural network (ANN) and fuzzy logic control (FLC) are implemented and their performance is analysed. Both the MPPT techniques are investigated in terms of efficiency and response and they are developed in MATLAB/Simulink environment. Their performance is investigated under variable irradiation conditions and found satisfactory for both the techniques.
  • Keywords
    fuzzy control; fuzzy logic; maximum power point trackers; neural nets; photovoltaic power systems; power system control; solar power stations; ANN; FLC; MATLAB/Simulink environment; artificial neural network; fuzzy logic based MPPT techniques; fuzzy logic control; maximum power point tracking technique; maximum power transfer; photovoltaic system; solar PV systems; Arrays; Artificial neural networks; Fuzzy logic; Maximum power point trackers; Radiation effects; Training; Voltage control; DC/DC Boost converter; Fuzzy logic; Maximum power point tracking (MPPT); Neural network; Photovoltaic (PV) system;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Power India International Conference (PIICON), 2014 6th IEEE
  • Print_ISBN
    978-1-4799-6041-5
  • Type

    conf

  • DOI
    10.1109/34084POWERI.2014.7117722
  • Filename
    7117722