Deep anomaly detection in horizontal axis wind turbines using Graph Convolutional Autoencoders for Multivariate Time series

Wind power is one of the fastest-growing renewable energy sectors instrumental in the ongoing decarbonization process. However, wind turbines are subjected to a wide range of dynamic loads which can cause more frequent failures and downtime periods, leading to ever-increasing attention to effective...

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Veröffentlicht in:	Energy and AI Jg. 8; S. 100145
Hauptverfasser:	Miele, Eric Stefan, Bonacina, Fabrizio, Corsini, Alessandro
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Ltd 01.05.2022 Elsevier
Schlagworte:	Condition monitoring Deep anomaly detection Early fault detection Graph Convolutional Autoencoder Multivariate Time series SCADA data Wind turbine Condition monitoring Graph Convolutional Autoencoder Deep anomaly detection SCADA data Early fault detection Wind turbine Multivariate Time series
ISSN:	2666-5468, 2666-5468
Online-Zugang:	Volltext
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