Research on Early Fault Diagnostic Method of Wind Turbines

Challenging environmental factors combined with high and turbulent winds make serious demands on wind turbines and result in significant component fault rates. In this paper, an early fault diagnostic research is conducted upon wind turbines. Firstly, the SCADA (Supervisory Control and Data Acquisition) system is used to analyze the units’ long-hour operating data, preparing for the further modeling work. Then the MSET (Multivariate State Estimation Technique) is adopted to estimate the temperature of the gear box and to obtain a result of high accuracy; with the Moving Window Calculation (MWC), the residual value between the estimated value and the real value is studied to get the dynamic trend of its average value; according to this trend in training, we define the threshold region of the residual mean value. Considering a man-made deviation in the observation vectors, faults of the gear box are simulated and studied. When the residual mean value curve exceeds the setting thresholds, an alert will be given to remind the operators of hidden problems in the unit. Research shows that this early diagnostic method is quite effective in detecting the abnormal performance of wind turbines in a real-time manner. DOI:  http://dx.doi.org/10.11591/telkomnika.v11i5.2457

[1]  Saeed Tavakoli,et al.  Fluctuations Mitigation of Variable Speed Wind Turbine through Optimized Centralized Controller , 2012 .

[2]  Yaoyu Li,et al.  A review of recent advances in wind turbine condition monitoring and fault diagnosis , 2009, 2009 IEEE Power Electronics and Machines in Wind Applications.

[3]  David Infield,et al.  Online wind turbine fault detection through automated SCADA data analysis , 2009 .

[4]  Wenxian Yang,et al.  Condition Monitoring of the Power Output of Wind Turbine Generators Using Wavelets , 2010, IEEE Transactions on Energy Conversion.

[5]  Mohamed Benbouzid,et al.  A Brief Status on Condition Monitoring and Fault Diagnosis in Wind Energy Conversion Systems , 2009 .

[6]  Michael G. Pecht,et al.  Multivariate State Estimation Technique for Remaining Useful Life Prediction of Electronic Products , 2007, AAAI Fall Symposium: Artificial Intelligence for Prognostics.

[7]  Peter Tavner,et al.  Condition monitoring and fault diagnosis of a wind turbine synchronous generator drive train , 2009 .

[8]  J. P. Herzog,et al.  Application of a model-based fault detection system to nuclear plant signals , 1997 .

[9]  Doaa M. Atia,et al.  Modeling and Control PV-Wind Hybrid System Based On Fuzzy Logic Control Technique , 2012 .

[10]  J. P. Herzog,et al.  MSET modeling of Crystal River-3 venturi flow meters. , 1998 .

[11]  R. W. King,et al.  Model-based nuclear power plant monitoring and fault detection: Theoretical foundations , 1997 .

[12]  F. Spinato,et al.  Condition Monitoring of Generators & Other Subassemblies in Wind Turbine Drive Trains , 2007, 2007 IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives.

[13]  Sung-Hoon Ahn,et al.  Condition monitoring and fault detection of wind turbines and related algorithms: A review , 2009 .

[14]  Kenny C. Gross,et al.  Advanced pattern recognition for detection of complex software aging phenomena in online transaction processing servers , 2002, Proceedings International Conference on Dependable Systems and Networks.

[15]  J. Wesley Hines,et al.  MSET PERFORMANCE OPTIMIZATION THROUGH REGULARIZATION , 2005 .

[16]  Wenxian Yang,et al.  Cost-Effective Condition Monitoring for Wind Turbines , 2010, IEEE Transactions on Industrial Electronics.

[17]  A-lan Jiang,et al.  Experimental study of acoustic emission characteristics of underwater concrete structures , 2008, 2008 Symposium on Piezoelectricity, Acoustic Waves, and Device Applications.