• DocumentCode
    1517167
  • Title

    Neural Network Estimation of Microgrid Maximum Solar Power

  • Author

    Chatterjee, Abir ; Keyhani, Ali

  • Author_Institution
    Ohio State Univ., Columbus, OH, USA
  • Volume
    3
  • Issue
    4
  • fYear
    2012
  • Firstpage
    1860
  • Lastpage
    1866
  • Abstract
    The integration of photovoltaic (PV) generating stations in the power grids requires the amount of power available from the PV to be estimated for power systems planning on yearly basis and operation control on daily basis. To determine the PV station maximum output power, the PV panels must be placed at an optimal tilt angle to absorb maximum energy from the sun. This optimal tilt angle is a nonlinear function of the location, time of year, ground reflectivity and the clearness index of the atmosphere. This paper proposes a neural network (NN) to estimate the optimal tilt angle at a given location and thus an estimate of the amount of energy available from the PV in a microgrid.
  • Keywords
    distributed power generation; neural nets; nonlinear functions; photovoltaic power systems; power engineering computing; power generation planning; power grids; NN; PV generating stations; PV panels; ground reflectivity; microgrid maximum solar power; neural network estimation; nonlinear function; optimal tilt angle; photovoltaic integration; power grids; power systems planning; sun; Artificial neural networks; Estimation; Photovoltaic systems; Power generation; Radiation effects; Terrestrial atmosphere; Irradiation; neural network; photovoltaic systems; power estimation; tilt angle;
  • fLanguage
    English
  • Journal_Title
    Smart Grid, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1949-3053
  • Type

    jour

  • DOI
    10.1109/TSG.2012.2198674
  • Filename
    6200398