• DocumentCode
    3764867
  • Title

    Comparison of ANN and ANFIS based MPPT Controller for grid connected PV systems

  • Author

    Ankita Arora;Prerna Gaur

  • Author_Institution
    Dept. of Instrumentation and Control Engineering, Netaji Subhas Institute of Technology (NSIT), University of Delhi, India
  • fYear
    2015
  • Firstpage
    1
  • Lastpage
    6
  • Abstract
    This paper presents comparison analysis of artificial neural network (ANN) and adaptive neuro fuzzy inference system (ANFIS) artificial intelligence (AI) based maximum power point tracking (MPPT) techniques for tracking maximum power from the Photovoltaic (PV) array. These algorithms are essential since PV arrays have non-linear characteristics with its firm dependence on changing solar irradiation and temperature. To increase the power extracted from solar panel, PV array must operate at a maximum power point (MPP) under given load conditions. Conventional algorithms such as Perturb and Observe (P&O) and Incremental-Conductance (Inc-Cond) suffers, with high oscillations during changing solar irradiation leading to low efficiency, therefore AI based techniques are designed and presented in this paper. ANFIS is more efficient in tracking MPP with less settling time, less overshoot, less oscillations and less time taken to track MPP than ANN based Controller.
  • Keywords
    "Artificial neural networks","Radiation effects","Maximum power point trackers","Oscillators","Training","Voltage control","Algorithm design and analysis"
  • Publisher
    ieee
  • Conference_Titel
    India Conference (INDICON), 2015 Annual IEEE
  • Electronic_ISBN
    2325-9418
  • Type

    conf

  • DOI
    10.1109/INDICON.2015.7443568
  • Filename
    7443568