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
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Zusammenfassung: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 Condition Monitoring strategies. In this paper, we propose a novel unsupervised deep anomaly detection framework to detect anomalies in wind turbines based on SCADA data. We introduce a promising neural architecture, namely a Graph Convolutional Autoencoder for Multivariate Time series, to model the sensor network as a dynamical functional graph. This structure improves the unsupervised learning capabilities of Autoencoders by considering individual sensor measurements together with the nonlinear correlations existing among signals. On this basis, we developed a deep anomaly detection framework that was validated on 12 failure events occurred during 20 months of operation of four wind turbines. The results show that the proposed framework successfully detects anomalies and anticipates SCADA alarms by outperforming other two recent neural approaches. [Display omitted] •Deep Learning approach for early anomaly detection in wind turbines.•Architecture based on Autoencoders and Graph Convolutional Networks.•Unsupervised and data-driven approach.•Training on healthy operating conditions of wind turbines.•Troubleshooting and faulty component identification through model residuals.
ISSN:2666-5468
2666-5468
DOI:10.1016/j.egyai.2022.100145