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All Journal Journal of Renewable Energy, Electrical, and Computer Engineering Jurnal Ekonomi dan Bisnis Digital (MINISTAL) Formosa Journal of Science and Technology (FJST) Formosa Journal of Applied Sciences (FJAS) Formosa Journal of Computer and Information Science Indonesian Journal of Advanced Research (IJAR) Journal of Electrical Engineering
Mahjabeen, Farhana
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Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Social Sciences

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Journal : Formosa Journal of Science and Technology (FJST)

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Intelligence in Photovoltaic Fault Identification and Diagnosis: A Systematic Review Mahjabeen, Farhana
Formosa Journal of Science and Technology Vol. 3 No. 10 (2024): October 2024
Publisher : PT FORMOSA CENDEKIA GLOBAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55927/fjst.v3i10.11552

Abstract

Undetected photovoltaic system faults can lead to significant energy losses, often exceeding 10%, necessitating efficient fault detection and diagnosis. Artificial intelligence, particularly machine learning and deep learning, offers promising solutions for real-time, high-volume fault detection and complex pattern recognition in PV systems. This research analyzes various PV fault detection studies, examining their objectives, methods, results, and the prevalence of ML/DL approaches. The analysis highlights the application of both classical ML algorithms, such as K-Nearest Neighbors and Random Forest, and advanced DL models, including Convolutional Neural Networks, for PV fault diagnosis.
Co-Authors Ahmad, Munir G.S., Kerimzade Garba, Auwal Hossain, Mohammad Hossain, Mohammad Amir Jain, Vishal Mahajan, Vinod Shantaram Patil, Dipak P. Rahman, Jami Yaseer Rahman, Taqi Yaseer Raza, Md. Adil