The DOI matches its Crossref record; 43 of the 43 references it cites were found in a registry or an index.
Using SCADA data for wind turbine condition monitoring – a review
Open accessAbstract
The ever increasing size of wind turbines and the move to build them offshore have accelerated the need for optimised maintenance strategies in order to reduce operating costs. Predictive maintenance requires detailed information on the condition of turbines. Due to the high costs of dedicated condition monitoring systems based on mainly vibration measurements, the use of data from the turbine supervisory control and data acquisition (SCADA) system is appealing. This review discusses recent research using SCADA data for failure detection and condition monitoring (CM), focussing on approaches which have already proved their ability to detect anomalies in data from real turbines. Approaches are categorised as (i) trending, (ii) clustering, (iii) normal behaviour modelling, (iv) damage modelling and (v) assessment of alarms and expert systems. Potential for future research on the use of SCADA data for advanced turbine CM is discussed.
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Cited by
Machine learning methods for wind turbine condition monitoring: A review
Stetco, A., Dinmohammadi, F., Zhao, X., Robu, V., Flynn, D., Barnes, M., Keane, J., & Nenadic, G. (2019) · Renewable Energy · doi:10.1016/j.renene.2018.10.047 · Cited by 831
Fault diagnosis of wind turbine based on Long Short-term memory networks
Lei, J., Liu, C., & Jiang, D. (2019) · Renewable Energy · doi:10.1016/j.renene.2018.10.031 · Cited by 507
Badihi, H., Zhang, Y., Jiang, B., Pillay, P., & Rakheja, S. (2022) · Proceedings of the IEEE · doi:10.1109/jproc.2022.3171691 · Cited by 319
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
Kong, Z., Tang, B., Deng, L., Liu, W., & Han, Y. (2020) · Renewable Energy · doi:10.1016/j.renene.2019.07.033 · Cited by 258
Metadata
- Authors
- Tautz‐Weinert, J., & Watson, S. J.
- Venue
- IET Renewable Power Generation
- Published
- 2016-12
- Volume
- 11
- Issue
- 4
- Pages
- 382-394
- ISSN
- 1752-1416 (ISSN-L)
- 1752-1424
- OpenAlex
- W2523553285
- Cited by
- 458
- References
- 43
- FWCI
- 21.60
- Type
- journal article
- Language
- en
- Open access
- green
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