Graph-Variational Convolutional Autoencoder-Based Fault Detection and Diagnosis for Photovoltaic Arrays

Solar energy is a critical renewable energy source, with solar arrays or photovoltaic systems widely used to convert solar energy into electrical energy. However, solar array systems can develop faults and may exhibit poor performance. Diagnosing and resolving faults within these systems promptly is...

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Veröffentlicht in:	Machines (Basel) Jg. 12; H. 12; S. 894
Hauptverfasser:	Arifeen, Murshedul, Petrovski, Andrei, Hasan, Md Junayed, Noman, Khandaker, Navid, Wasib Ul, Haruna, Auwal
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Basel MDPI AG 01.12.2024
Schlagworte:	Arrays Artificial neural networks Deep learning Energy Error analysis Error detection Fault detection Fault diagnosis Faults Fourier transforms graph convolutional network Machine learning Neural networks Performance evaluation photovoltaic array Renewable energy sources Sensor arrays Sensors solar array Solar arrays Solar collectors Solar energy Solar energy conversion Solar energy industry Spatiotemporal data Spread spectrum System reliability Time series variational autoencoder New Jersey
ISSN:	2075-1702, 2075-1702
Online-Zugang:	Volltext
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