1. Wind turbine induction generator bearing fault detection using stator current analysis By School of Electrical and Electronic Engineering The University of Manchester D.S. Vilchis-Rodriguez, S. Djurovic, A.C. Smith

2. Content 1.Wind generator failure figures 2.Ball bearing frequencies 3.Mathematical model 4.Simulation results 5.Experimental results 6.Fault detection improvement 7.Conclusions

3. Wind turbine reliability Feng Y. and Tavner P., “Introduction to Wind Turbines and their Reliability & Availability”, Warsaw, EWEC 2010, 2010.

4. Wind generator failure occurrence 1-2 MW>2 MW Alewine K. and Chen W., “Wind Turbine Generator Failure Modes Analysis and Occurrence”, Windpower 2010, Dallas, Texas, May 24-26, 2010.

5. Rolling bearing race frequencies Outer raceInner race

6. Bearing fault mechanical effects Shaft displacementRolling element drop

7. Air-gap modulation Air-gap variationsPeriodic eccentricity

8. IG modelling for condition monitoring purposes Based on coupled-circuit approach Localized bearing faults are modelled as temporary eccentricity variations Axial asymmetry is taken into account in the model by averaging both machine ends eccentricity This approach makes it possible to analyze with detail incipient bearing faults

9. Bearing fault simulation results Stator current frequency spectrum Principal bearing fault frequency detail

10. Test rig layout Laboratory test bed (viewed from above) Load side bearing

11. Test rig description Artificial bearing faultTest rig bearing data Drive-endNon-drive-end SKF 6313SKF 6214 N b = 8N b = 10 f o =3.07f r f o =4.11f r f i =4.93f r f i =5.89f r

12. Bearing fault Measured Frequency spectrum Vibration spectrum Stator line current spectrum

13. Instantaneous complex current signal

14. Stator current and current envelope frequency spectrums Stator current spectrum Complex signal magnitude spectrum

15. Complex signal magnitude frequency spectrum per phase Stator currents Complex signal magnitude spectrum

16. Instantaneous negative sequence magnitude

17. Instantaneous symmetrical components Real valued instantaneous symmetrical components Complex valued instantaneous symmetrical components

18. Complex signals frequency spectrum a) Current envelope spectrum average b) Complex valued Instantaneous negative sequence spectrum c) Real valued Instantaneous negative sequence spectrum

19. Fault severity analysis Artificial bearing fault Fault frequency amplitude variation

20. Conclusions An IG analytical model was developed and a commercial machine test rig was used to verify the findings Research shows that there are frequency components in IG steady state stator current that are directly related to existence of bearing fault. Simulation and experimental data indicate that conventional CSA is not well suited for bearing fault detection. The use of complex signals is shown to considerably improve the fault detection using stator current analysis.

21. Thank You