Data-driven design of robust fault detection system for wind turbines

In this paper, a robust data-driven fault detection approach is proposed with application to a wind turbine benchmark. The main challenges of the wind turbine fault detection lie in its nonlinearity, unknown disturbances as well as significant measurement noise. To overcome these difficulties, a dat...

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Bibliographic Details
Published inMechatronics (Oxford) Vol. 24; no. 4; pp. 298 - 306
Main Authors Yin, Shen, Wang, Guang, Karimi, Hamid Reza
Format Journal Article
LanguageEnglish
Published Elsevier Ltd 01.06.2014
Subjects
Benchmarking
Data-driven
Design engineering
Disturbances
Fault detection
Optimization
Optimization criterion
Performance index
Performance indices
Robustness
Wind turbine
Wind turbines
Robustness
Data-driven
Fault detection
Wind turbine
Performance index
Optimization criterion
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Summary:In this paper, a robust data-driven fault detection approach is proposed with application to a wind turbine benchmark. The main challenges of the wind turbine fault detection lie in its nonlinearity, unknown disturbances as well as significant measurement noise. To overcome these difficulties, a data-driven fault detection scheme is proposed with robust residual generators directly constructed from available process data. A performance index and an optimization criterion are proposed to achieve the robustness of the residual signals related to the disturbances. For the residual evaluation, a proper evaluation approach as well as a suitable decision logic is given to make a correct final decision. The effectiveness of the proposed approach is finally illustrated by simulations on the wind turbine benchmark model.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:0957-4158
1873-4006
DOI:10.1016/j.mechatronics.2013.11.009