LAPSE:2022.0152
Published Article
LAPSE:2022.0152
An Overview on Fault Diagnosis, Prognosis and Resilient Control for Wind Turbine Systems
Zhiwei Gao, Xiaoxu Liu
November 27, 2022
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.
Keywords
condition monitoring, energy conversion systems, fault diagnosis, fault prognosis, resilient control, wind turbine
Suggested Citation
Gao Z, Liu X. An Overview on Fault Diagnosis, Prognosis and Resilient Control for Wind Turbine Systems. (2022). LAPSE:2022.0152
Author Affiliations
Gao Z: Faculty of Engineering and Environment, University of Northumbria at Newcastle, Newcastle upon Tyne NE1 8ST, UK [ORCID]
Liu X: Sino-German College of Intelligent Manufacturing, Shenzhen Technology University, Shenzhen 518118, China
Journal Name
Processes
Volume
9
Issue
2
First Page
300
Year
2021
Publication Date
2021-02-05
Published Version
ISSN
2227-9717
Version Comments
Original Submission
Other Meta
PII: pr9020300, Publication Type: Review
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LAPSE:2022.0152
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doi:10.3390/pr9020300
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Nov 27, 2022
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CC BY 4.0
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[v1] (Original Submission)
Nov 27, 2022
 
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Nov 27, 2022
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https://psecommunity.org/LAPSE:2022.0152
 
Original Submitter
Mina Naeini
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