Wind Turbine Gearbox Condition Monitoring with AAKR and Moving Window Statistic Methods

Peng Guo and Nan Bai
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Peng Guo: School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China
Nan Bai: School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China

Energies, 2011, vol. 4, issue 11, 1-17

Abstract: Condition Monitoring (CM) of wind turbines can greatly reduce the maintenance costs for wind farms, especially for offshore wind farms. A new condition monitoring method for a wind turbine gearbox using temperature trend analysis is proposed. Autoassociative Kernel Regression (AAKR) is used to construct the normal behavior model of the gearbox temperature. With a proper construction of the memory matrix, the AAKR model can cover the normal working space for the gearbox. When the gearbox has an incipient failure, the residuals between AAKR model estimates and the measurement temperature will become significant. A moving window statistical method is used to detect the changes of the residual mean value and standard deviation in a timely manner. When one of these parameters exceeds predefined thresholds, an incipient failure is flagged. In order to simulate the gearbox fault, manual temperature drift is added to the initial Supervisory Control and Data Acquisitions (SCADA) data. Analysis of simulated gearbox failures shows that the new condition monitoring method is effective.

Keywords: wind turbine condition monitoring; gearbox; Autoassociative Kernel Regression; residual analysis; moving window statistics (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: 2011
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (14)

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