A Conditional Convolutional Autoencoder-Based Method for Monitoring Wind Turbine Blade Breakages

The wind turbine blade breakage is a catastrophic failure to a wind farm. Its earlier detection is critical to prevent the unscheduled downtime and loss of whole assets. This article presents a conditional convolutional autoencoder-based monitoring method, which is of twofold, for identifying wind t...

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Veröffentlicht in:IEEE transactions on industrial informatics Jg. 17; H. 9; S. 6390 - 6398
Hauptverfasser: Yang, Luoxiao, Zhang, Zijun
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 01.09.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Blades
Catastrophic events
Condition monitoring
data mining
Data models
Downtime
fault detection
Monitoring
Multivariate analysis
neural networks (NNs)
Principal component analysis
Process controls
Reconstruction
Statistical process control
Supervisory control and data acquisition
System dynamics
Turbine blades
wind farm
Wind farms
Wind power
Wind turbines
ISSN:1551-3203, 1941-0050
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Zusammenfassung:The wind turbine blade breakage is a catastrophic failure to a wind farm. Its earlier detection is critical to prevent the unscheduled downtime and loss of whole assets. This article presents a conditional convolutional autoencoder-based monitoring method, which is of twofold, for identifying wind turbine blade breakages. First, a novel conditional convolutional autoencoder taking a multivariate set of data as input is developed to derive reconstruction errors, which reflect changes of system dynamics caused by impending blade breakages. Next, a statistical process control principle is applied to develop boundaries for triggering blade breakage alarms based on reconstruction errors. The effectiveness of the conditional convolutional autoencoder-based method is validated with datasets collected by supervisory control and data acquisition systems installed in multiple commercial wind farms. We also demonstrate advantages of the conditional convolutional autoencoder-based monitoring method by benchmarking against the classical autoencoder and conditional autoencoder-based monitoring methods.
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ISSN:1551-3203
1941-0050
DOI:10.1109/TII.2020.3011441