An Overview on Fault Diagnosis, Prognosis and Resilient Control for Wind Turbine Systems

Gao, Zhiwei and Liu, Xiaoxu (2021) An Overview on Fault Diagnosis, Prognosis and Resilient Control for Wind Turbine Systems. Processes, 9 (2). p. 300. ISSN 2227-9717

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Official URL: https://doi.org/10.3390/pr9020300

Abstract

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.

Item Type: Article
Additional Information: Funding: This research was funded by the National Nature Science Foundation of China, grant number 61673074; Guangdong Basic and Applied Basic Research Foundation, grant number 1515110234; and Nature Science Foundation of Top Talent of SZTU, grant number 2020106.
Uncontrolled Keywords: wind turbine; energy conversion systems; condition monitoring; fault diagnosis; fault prognosis; resilient control
Subjects: F300 Physics
H200 Civil Engineering
H800 Chemical, Process and Energy Engineering
Department: Faculties > Engineering and Environment > Mathematics, Physics and Electrical Engineering
Depositing User: Elena Carlaw
Date Deposited: 05 Feb 2021 12:27
Last Modified: 31 May 2021 14:43
URI: http://nrl.northumbria.ac.uk/id/eprint/45380

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