• DocumentCode
    187261
  • Title

    A computational intelligence approach to solar panel modelling

  • Author

    Ferrari, Silvia ; Lazzaroni, M. ; Piuri, V. ; Salman, A. ; Cristaldi, L. ; Faifer, Marco ; Toscani, Sergio

  • Author_Institution
    Univ. degli Studi di Milano, Milan, Italy
  • fYear
    2014
  • fDate
    12-15 May 2014
  • Firstpage
    1261
  • Lastpage
    1266
  • Abstract
    The power produced by a solar panel depends on several parameters. In order to optimize the production, the ability to operate in the Maximum Power Point (MPP) condition is requested. The ability to identify and reach the MPP condition is therefore critical to an efficient conversion of the photovoltaic energy. In this paper, several computational intelligence paradigms are challenged in the task of identifying the MPP power from the working condition directly measurable from the solar panel, such as the voltage, V, the current, I, and the temperature, T, of the panel.
  • Keywords
    maximum power point trackers; solar cells; MPP condition; computational intelligence approach; maximum power point condition; photovoltaic energy; solar panel modelling; Computational intelligence; Computational modeling; Current measurement; Predictive models; Temperature measurement; Testing; Training;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Instrumentation and Measurement Technology Conference (I2MTC) Proceedings, 2014 IEEE International
  • Conference_Location
    Montevideo
  • Type

    conf

  • DOI
    10.1109/I2MTC.2014.6860947
  • Filename
    6860947