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

Ryotaro Torikai, Hooman Farzaneh

研究成果: ジャーナルへの寄稿会議記事査読

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.

本文言語英語
ページ(範囲)321-326
ページ数6
ジャーナルInternational Exchange and Innovation Conference on Engineering and Sciences
9
DOI
出版ステータス出版済み - 2023
イベント9th International Exchange and Innovation Conference on Engineering and Sciences, IEICES 2023 - Kyushu, 日本
継続期間: 10月 19 202310月 20 2023

!!!All Science Journal Classification (ASJC) codes

  • 一般

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