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

DOI HANDLE オープンアクセス
  • 鳥飼 凌太郎
    九州大学大学院総合理工学府
  • Farzaneh Hooman
    九州大学大学院総合理工学府 九州大学グリーンテクノロジー研究教育センター

抄録

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.

収録刊行物

詳細情報 詳細情報について

  • CRID
    1390861305866501760
  • DOI
    10.5109/7157996
  • HANDLE
    2324/7157996
  • ISSN
    24341436
  • 本文言語コード
    en
  • データソース種別
    • JaLC
    • IRDB
    • Crossref
  • 抄録ライセンスフラグ
    使用可

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