A Conditional Convolutional Autoencoder-Based Method for Monitoring Wind Turbine Blade Breakages
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
Original language | English |
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Article number | 9146603 |
Pages (from-to) | 6390-6398 |
Journal / Publication | IEEE Transactions on Industrial Informatics |
Volume | 17 |
Issue number | 9 |
Online published | 23 Jul 2020 |
Publication status | Published - Sept 2021 |
Link(s)
Abstract
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.
Research Area(s)
- Condition monitoring, data mining, fault detection, neural networks (NNs), wind farm
Citation Format(s)
A Conditional Convolutional Autoencoder-Based Method for Monitoring Wind Turbine Blade Breakages. / Yang, Luoxiao; Zhang, Zijun.
In: IEEE Transactions on Industrial Informatics, Vol. 17, No. 9, 9146603, 09.2021, p. 6390-6398.
In: IEEE Transactions on Industrial Informatics, Vol. 17, No. 9, 9146603, 09.2021, p. 6390-6398.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review