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Online wind turbine fault detection through automated SCADA data analysis
Abstract
This paper describes a set of anomaly‐detection techniques and their applicability to wind turbine fault identification. It explains how the anomaly‐detection techniques have been adapted to analyse supervisory control and data acquisition data acquired from a wind farm, automating and simplifying the operators' analysis task by interpreting the volume of data available. The techniques are brought together into one system to collate their output and provide a single decision support environment for an operator. The framework used is a novel multi‐agent system architecture that offers the opportunity to corroborate the output of the various interpretation techniques in order to improve the accuracy of fault detection. The results presented demonstrate that the interpretation techniques can provide performance assessment and early fault identification, thereby giving the operators sufficient time to make more informed decisions regarding the maintenance of their machines. Copyright © 2009 John Wiley & Sons, Ltd.
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Cites
Intelligent agents: theory and practice
Wooldridge, M., & Jennings, N. R. (1995) · The Knowledge Engineering Review · doi:10.1017/s0269888900008122 · Cited by 6,810
Cited by
Wind Turbine Fault Detection Using a Denoising Autoencoder With Temporal Information
Jiang, G., Xie, P., He, H., & Yan, J. (2018) · IEEE/ASME Transactions on Mechatronics · doi:10.1109/tmech.2017.2759301 · Cited by 288
Monitoring wind turbine gearboxes
Feng, Y., Qiu, Y., Crabtree, C. J., Long, H., & Tavner, P. J. (2013) · Wind Energy · doi:10.1002/we.1521 · Cited by 253
An unsupervised anomaly-based detection approach for integrity attacks on SCADA systems
Almalawi, A., Yu, X., Tari, Z., Fahad, A., & Khalil, I. (2014) · Computers & Security · doi:10.1016/j.cose.2014.07.005 · Cited by 113
Lin, Z., Duan, D., Yang, Q., Hong, X., Cheng, X., Yang, L., & Cui, S. (2020) · IEEE Access · doi:10.1109/access.2020.3009385 · Cited by 21
Add-On Anomaly Threshold Technique for Improving Unsupervised Intrusion Detection on SCADA Data
Almalawi, A., Fahad, A., Tari, Z., Khan, A. I., Alzahrani, N., Bakhsh, S. T., Alassafi, M. O., Alshdadi, A., & Qaiyum, S. (2020) · Electronics · doi:10.3390/electronics9061017 · Cited by 14
Metadata
- Authors
- Zaher, A., McArthur, S. D. J., Infield, D. G., & Patel, Y.
- Venue
- Wind Energy
- Publisher
- Wiley
- Published
- 2009-01-20
- Volume
- 12
- Issue
- 6
- Pages
- 574-593
- ISSN
- 1095-4244 (ISSN-L)
- 1099-1824
- OpenAlex
- W2127064699
- Cited by
- 464
- References
- 1
- FWCI
- 23.40
- Type
- journal article
- Language
- en
- Open access
- closed
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- Zaher, A., McArthur, S. D. J., Infield, D. G., & Patel, Y. (2009). Online wind turbine fault detection through automated SCADA data analysis. Wind Energy. https://doi.org/10.1002/we.319
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
