SCADA-Based Condition Monitoring

1. SCADA-Based Condition Monitoring Joining advanced analytic techniques with turbine engineering Michael WilkinsonEWEA 2013, Vienna

2. SCADA-Based Condition Monitoring What is it? Failure detection algorithm that uses existing SCADA data Uses an established relationship between SCADA signals to detect when a component is operating abnormally Compares suspected failures to a database of known issues to determine likelihood of an emerging problem What is it not? High-frequency vibration monitoring An automatic algorithm

3. SCADA-Based Condition Monitoring What signals are available? Nacelle anemometer wind speed Yaw angle Pitch angle Main bearing temp Rotor rotational speed Gearbox bearing temps Generator bearing temps Gearbox oil sump temp Generator rotational speed Winding temps Gearbox Generator Gate temperatures Exported power Hub and pitch system Main bearing Phase Voltages & Currents Winding temps Powerconverter Transformer Nacelle internal ambient temp Cooling system temps External ambient temp

4. Comparison of Methods: Temperature Trending • Simple method • Readily applied to many datasets • Low reliability during intermittent or changing operational modes

5. Comparison of Methods: Artificial Neural Networks • Learning algorithm used to reveal patterns in data or model complex relationships between variables • More sensitive to ‘abnormal’ behaviour • Inability to identify nature of the operational issue • Results difficult to interpret

6. Comparison of Methods: Physical Model Depends on nacelle and external temperature and cooling system duty Heat Loss to Surroundings Heat Loss to Cooling System WIND TURBINE DRIVETRAIN COMPONENT Model using export power Energy Input Energy Output Model using nws3 Include ambient temperature and pressure if available. T Frictional Losses Dependent on shaft speed (use rotor speedor generator speed in model) SCADA System Model inputs: Nws3, power, rotor speed, external temp, cooling system temp Model output: Component temp

7. Comparison of Methods: Conclusions

8. Comparison of Methods: Conclusions

9. Comparison of Methods: Conclusions

10. Comparison of Methods: Conclusions

11. Comparison of Methods: Conclusions

12. Comparison of Methods: Conclusions

13. Validation Study • Series of blind tests were conducted • Historical data • Engineer given no indication of known failures • Suspected impending failures documented • 472 turbine-years of data considered • Compared against service records and site management reports

14. Validation Study: Example Results • Both charts show different signals on same turbine: TACTUAL –TMODELLED TACTUAL –TMODELLED

15. Validation Study: Results

16. Validation Study: Results

17. Validation Study: Results • Two thirds of failures detected in advance

18. Validation Study: Results • Majority of failures detected 4 to 12 months in advance

19. Summary & Conclusions • Comparison of methods: • Temperature trending, physical model and artificial neural network methods compared • Physical model identified as most promising

20. Summary & Conclusions • Comparison of methods: • Temperature trending, physical model and artificial neural network methods compared • Physical model identified as most promising • Validation study performed: • Two thirds of failures detected in advance • Majority of failures detected 4 to 12 months in advance

21. Summary & Conclusions • Comparison of methods: • Temperature trending, physical model and artificial neural network methods compared • Physical model identified as most promising • Validation study performed: • Two thirds of failures detected in advance • Majority of failures detected 4 to 12 months in advance • Overall conclusions: • Quick implementation – no additional monitoring hardware required • Pro-active maintenance/repair activities to be scheduled and planned • Targeted inspections possible

22. Questions or comments?Michael WilkinsonGL Garrad Hassan+44 117 972 9900michael.wilkinson@gl-garradhassan.com