The DOI matches its Crossref record; 64 of the 64 references it cites were found in a registry or an index.
An Overview on Fault Diagnosis, Prognosis and Resilient Control for Wind Turbine Systems
Open accessAbstract
Wind energy is contributing to more and more portions in the world energy market. However, one deterrent to even greater investment in wind energy is the considerable failure rate of turbines. In particular, large wind turbines are expensive, with less tolerance for system performance degradations, unscheduled system shut downs, and even system damages caused by various malfunctions or faults occurring in system components such as rotor blades, hydraulic systems, generator, electronic control units, electric systems, sensors, and so forth. As a result, there is a high demand to improve the operation reliability, availability, and productivity of wind turbine systems. It is thus paramount to detect and identify any kinds of abnormalities as early as possible, predict potential faults and the remaining useful life of the components, and implement resilient control and management for minimizing performance degradation and economic cost, and avoiding dangerous situations. During the last 20 years, interesting and intensive research results were reported on fault diagnosis, prognosis, and resilient control techniques for wind turbine systems. This paper aims to provide a state-of-the-art overview on the existing fault diagnosis, prognosis, and resilient control methods and techniques for wind turbine systems, with particular attention on the results reported during the last decade. Finally, an overlook on the future development of the fault diagnosis, prognosis, and resilient control techniques for wind turbine systems is presented.
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Cites
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Cited by
A critical review of cyber-physical security for building automation systems
Li, G., Ren, L., Fu, Y., Yang, Z., Adetola, V., Wen, J., Zhu, Q., Wu, T., Candan, K. S., & O'Neill, Z. (2023) · Annual Reviews in Control · doi:10.1016/j.arcontrol.2023.02.004 · Cited by 71
De Kooning, J. D. M., Stockman, K., De Maeyer, J., Jarquin-Laguna, A., & Vandevelde, L. (2021) · Processes · doi:10.3390/pr9122224 · Cited by 57
Metadata
- Authors
- Gao, Z., & Liu, X.
- Venue
- Processes
- Publisher
- MDPI AG
- Published
- 2021-02-05
- Volume
- 9
- Issue
- 2
- Pages
- 300
- ISSN
- 2227-9717 (ISSN-L)
- OpenAlex
- W3126685764
- Cited by
- 281
- References
- 64
- FWCI
- 24.67
- Type
- journal article
- Language
- en
- Open access
- gold
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