Design and development of an Artificial Neural Network-based Maximum Power Point Tracker (ANN_MPPT) for the residential solar photovoltaic

DOI HANDLE Open Access
  • Torikai Ryotaro
    Interdisciplinary Graduate School of Engineering Sciences, Kyushu University
  • Farzaneh Hooman
    Interdisciplinary Graduate School of Engineering Sciences, Kyushu University Transdisciplinary Research and Education Center for Green Technologies, Kyushu University

Abstract

In recent years, solar power generation systems have been evolving from the perspective of mitigating global warming. The Maximum Power Point Tracking (MPPT) method is a notable method garnering attention. In this study, two MPPT methods were compared using MATLAB/Simulink. One method employed the perturb and observe technique, while the other utilized Artificial Neural Networks (ANN). The comparison results revealed that the power generation system using the ANN-based approach generated more electricity than the perturb and observe method.

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Details 詳細情報について

  • CRID
    1390861305866501760
  • DOI
    10.5109/7157996
  • HANDLE
    2324/7157996
  • ISSN
    24341436
  • Text Lang
    en
  • Data Source
    • JaLC
    • IRDB
    • Crossref
  • Abstract License Flag
    Allowed

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