Fault detection and diagnosis of grid-connected photovoltaic systems using energy valley optimizer based lightweight CNN and wavelet transform

Abstract Early fault detection and diagnosis of grid-connected photovoltaic systems (GCPS) is imperative to improve their performance and reliability. Low-cost edge devices have emerged as innovative solutions for real-time monitoring, reducing latency, and improving response times. In this work, a...

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Auteurs principaux:	Ali Teta, Belkacem Korich, Derradji Bakria, Nadji Hadroug, Abdelaziz Rabehi, Mohammad Alsharef, Mohit Bajaj, Ievgen Zaitsev, Sherif S. M. Ghoneim
Format:	Artigo
Langue:	Inglês
Publié:	Nature Portfolio 2024-08-01
Collection:	Scientific Reports
Sujets:	Grid-connected PV systems Faults diagnosis Continuous wavelet transform Convolutional neural networks
Accès en ligne:	https://doi.org/10.1038/s41598-024-69890-7
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