TY - JOUR
T1 - Design and development of an Artificial Neural Network-based Maximum Power Point Tracker (ANN_MPPT) for the residential solar photovoltaic
AU - Torikai, Ryotaro
AU - Farzaneh, Hooman
N1 - Publisher Copyright:
© 2023, Kyushu University. All rights reserved.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85184323131&partnerID=8YFLogxK
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U2 - 10.5109/7157996
DO - 10.5109/7157996
M3 - Conference article
AN - SCOPUS:85184323131
SN - 2434-1436
VL - 9
SP - 321
EP - 326
JO - International Exchange and Innovation Conference on Engineering and Sciences
JF - International Exchange and Innovation Conference on Engineering and Sciences
T2 - 9th International Exchange and Innovation Conference on Engineering and Sciences, IEICES 2023
Y2 - 19 October 2023 through 20 October 2023
ER -