System-wide anomaly detection in wind turbines using deep autoencoders

Niklas Renström, Pramod Bangalore and Edmund Highcock

Renewable Energy, 2020, vol. 157, issue C, 647-659

Abstract: Using supervisory control and data acquisition (SCADA) data to detect faults in wind turbines (WTs) has gained interest over the last few years. The SCADA system is installed by default for modern WTs and a condition monitoring system can be employed without installing additional measurement devices, which ensures a cost-effective solution for operators. Most systems developed today monitor only one component at a time. To cover all aspects of a WT’s operation one would therefore have to use one model for each component. Such a system would quickly become unwieldy and expensive to manage in practice.

Keywords: Wind turbine; Condition monitoring system; Anomaly detection; SCADA; Autoencoder; Predictive maintenance (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (9)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:157:y:2020:i:c:p:647-659

DOI: 10.1016/j.renene.2020.04.148

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