6533b82bfe1ef96bd128e2c1
RESEARCH PRODUCT
Data-driven design of robust fault detection system for wind turbines
Guang WangHamid Reza KarimiShen Yinsubject
EngineeringWind powerbusiness.industryMechanical EngineeringControl engineeringComputer Science Applications1707 Computer Vision and Pattern RecognitionResidualData-drivenTurbineFault detection and isolationComputer Science ApplicationsData-drivenNonlinear systemOptimization criterionPerformance indexControl and Systems EngineeringRobustness (computer science)Control theoryElectrical and Electronic EngineeringbusinessDecision tableRobustnessFault detectionWind turbineData-driven; Fault detection; Optimization criterion; Performance index; Robustness; Wind turbine; Mechanical Engineering; Computer Science Applications1707 Computer Vision and Pattern Recognition; Electrical and Electronic Engineeringdescription
Abstract 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.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2014-06-01 |