A Review of Fault Diagnosis, Status Prediction, and Evaluation Technology for Wind Turbines

Fanghong Zhang, Mingsong Chen, Yuze Zhu, Kai Zhang and Qingan Li
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Fanghong Zhang: The National Center for Applied Mathematics in Chongqing, Chongqing Normal University, Chongqing 401331, China
Mingsong Chen: The National Center for Applied Mathematics in Chongqing, Chongqing Normal University, Chongqing 401331, China
Yuze Zhu: The National Center for Applied Mathematics in Chongqing, Chongqing Normal University, Chongqing 401331, China
Kai Zhang: CSIC Haizhuang Windpower Co., Ltd., Chongqing 401122, China
Qingan Li: University of Chinese Academy of Sciences, Beijing 100049, China

Energies, 2023, vol. 16, issue 3, 1-15

Abstract: With the rapid development and increasing energy production capacity of high-power wind turbines, a corresponding increase in maintenance requirements has been observed. Reducing the failure rate of wind turbines is a critical objective, alongside decreasing affiliated operation and maintenance costs. This review focuses on the status monitoring, fault diagnosis, fault prediction, and status evaluation of wind turbines. The early fault diagnosis of wind turbines is explored with regard to existing condition monitoring technology. Moreover, the current mathematics-based fault diagnosis and smart fault diagnosis technologies are further explored. Through comprehensive investigation, this paper summarizes the research status of wind turbine fault prediction and complete machine status evaluation, conclusively presenting relevant research points and trends in the fault diagnosis, fault prediction, and status assessment of high-power wind turbines.

Keywords: wind turbines; condition monitoring; mathematical fault diagnosis; smart fault diagnosis; fault prediction; status evaluation (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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