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

Mengnan Cao, Yingning Qiu, Yanhui Feng, Hao Wang and Dan Li
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Mengnan Cao: School of Energy and Power Engineering, Nanjing University of Science and Technology, No. 200, Xiaolingwei, Nanjing 210094, China
Yingning Qiu: School of Energy and Power Engineering, Nanjing University of Science and Technology, No. 200, Xiaolingwei, Nanjing 210094, China
Yanhui Feng: School of Energy and Power Engineering, Nanjing University of Science and Technology, No. 200, Xiaolingwei, Nanjing 210094, China
Hao Wang: School of Energy and Power Engineering, Nanjing University of Science and Technology, No. 200, Xiaolingwei, Nanjing 210094, China
Dan Li: School of Energy and Power Engineering, Nanjing University of Science and Technology, No. 200, Xiaolingwei, Nanjing 210094, China

Energies, 2016, vol. 9, issue 10, 1-18

Abstract: 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.

Keywords: wind turbine; fault diagnosis; unscented Kalman filter; SCADA data (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: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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