Study of Wind Turbine Fault Diagnosis Based on Unscented Kalman Filter and SCADA Data

Effective wind turbine fault diagnostic algorithms are crucial for wind turbine intelligent condition monitoring. An unscented Kalman filter approach is proposed to successfully detect and isolate two types of gearbox failures of a wind turbine in this paper. The state space models are defined for t...

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Bibliographic Details
Published in:Energies (Basel) 2016-10, Vol.9 (10), p.847-847
Main Authors: Cao, Mengnan, Qiu, Yingning, Feng, Yanhui, Wang, Hao, Li, Dan
Format: Article
Language:English
Subjects:
Algorithms
Condition monitoring
Failure
Fault detection
Fault diagnosis
Faults
Gearboxes
Kalman filters
SCADA data
Signal processing
Simulation
Supervisory control and data acquisition
Turbines
unscented Kalman filter
wind turbine
Wind turbines
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Summary:Effective wind turbine fault diagnostic algorithms are crucial for wind turbine intelligent condition monitoring. An unscented Kalman filter approach is proposed to successfully detect and isolate two types of gearbox failures of a wind turbine in this paper. The state space models are defined for the unscented Kalman filter model by a detailed wind turbine nonlinear systematic principle analysis. The three failure modes being studied are gearbox damage, lubrication oil leakage and pitch failure. The results show that unscented Kalman filter model has special response to online input parameters under different fault conditions. Such property makes it effective on fault identification. It also shows that properly defining unscented Kalman filter state space vectors and control vectors are crucial for improving its sensitivity to different failures. Online fault detection capability of this approach is then proved on SCADA data. The developed unsented Kalman filter model provides an effective way for wind turbine fault detection using supervisory control and data acquisition data. This is essential for further intelligent WT condition monitoring.
ISSN:1996-1073
1996-1073
DOI:10.3390/en9100847