1. RESULTS OF THE CONTRIBUTIONS TO THE COMPETITION ON WIND TURBINE FAULT DETECTION AND ISOLATION Presented by Peter Fogh Odgaard* At Wind Turbine Control Symposium at Aalborg University 28 th -29 th November 2011 *kk-electronic a/s, Denmark, peodg@kk-electronic.compeodg@kk-electronic.com Contributions from: Stoustrup, J., Kinnaert, M., Laouti, N., Sheibat- Othman, N., Othman, S., Zhang, X., Zhang, Q., Zhao, S., Ferrari, R. M., Polycarpou, M. M., Parisini, T., Ozdemir, A., Seiler, P., Balas, G., Chen, W., Ding, S., Sari, A., Naik, A., Khan, A., Yin, S., Svard, C. & Nyberg, M.

2. Outline Motivation FDI/FTC Benchmark Model and Competition Description of Selected Contributions Results of the Selected Contributions Planned Continuations Is the Objectives meet?

3. Motivation Increased reliability is of high important in order to minimize cost of energy of wind turbines. Fault Detection and isolation (FDI) and Fault Tolerant Control (FTC) are some of the important solutions in obtaining this.

4. Objective The benchmark model 1 and competition should: –To attract attention from Academia to the FDI & FTC problem on wind turbines. –Provide a platform some how relating to wind turbines which all can use, and which can be used for comparisons. –A part of showing the potential of FDI and FTC in Wind Turbines. 1 Odgaard, P.; Stoustrup, J. & Kinnaert, M. Fault Tolerant Control of Wind Turbines – a benchmark model Proceedings of the 7th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes, 2009, 155-160

5. FDI/FTC Benchmark A generic 4.8MW wind turbine is used.

6. Model Details Pitch actuators Second order transfer function with constraints for each blade. Close loop system. Converter First order transfer function with constraints. Close loop system. Drive train modeled with a 3 state model. Including inertias from generator and rotor. Simple Cp curve based aerodynamic model Sensors modeled by band limited random noise blocks.

7. Wind Speed Input

8. Faults

9. Faults (II)

10. Faults III Seven additional tests were performed with time shifted fault occurrences, resulting in other point of operations for the faults.

11. FDI Requirements Requirements to detection times –Sensors 10Ts –Converter 3Ts –Hydraulic oil leakage 8Ts –Air in oil 100Ts Requirement to interval between false positive detections – 100000 samples, and three successive detections are accepted. All faults should be detected.

12. Gausian Kernel Support Vector Machine solution 2 2 Laouti, N., Sheibat-Othman, N. & Othman, S., Support Vector Machines for Fault Detection in Wind Turbines Proceedings of IFAC World Congress 2011, 2011, 7067-7072

13. Estimation Based solution 3 A fault detection estimator is designed to detect faults, and an additional bank of N isolation estimators are designed to isolate the faults. The estimators used for fault detection and isolation are designed based on the provided models including model parameters. Each isolation estimator is designed based on a particular fault scenario under consideration. 3 Zhang, X., Zhang, Q., Zhao, S., Ferrari, R. M., Polycarpou, M. M. & Parisini, T. Fault Detection and Isolation of the Wind Turbine Benchmark: An Estimation-Based Approach. Proceedings of IFAC World Congress 2011, 2011, 8295-8300

14. Up-Down Counter solution 4 Up-down counters are used in this solution for decision of fault detection and isolation based on residuals for each of the faults. The fault detection and isolation residuals are based on residuals obtained by physical redundancy, parity equations and different filters. Up-down counters based decisions depends on discrete-time dynamics and amplitude of the residuals. 4 Ozdemir, A., Seiler, P. & Balas, G. Wind Turbine Fault Detection Using Counter-Based Residual Thresholding Proceedings of IFAC World Congress 2011, 2011, 8289-8294

15. Combined Observer and Kalman Filter solution 5 A diagnostic observer based residual generator is used for the faults in the Drive Train, in which the wind speed also is considered as a disturbance. It is decoupled from the disturbance and optimal. A Kalman filter based scheme is designed for the other two subsystems. GLR test and cumulative variance index are used for fault decision. Filter banks are used for fault isolation. 5 Chen, W., Ding, S., Sari, A., Naik, A., Khan, A. & Yin, S. Observer-based FDI Schemes for Wind Turbine Benchmark Proceedings of IFAC World Congress 2011, 2011, 7073- 7078

16. General Fault Model solution 6 An automatic generated solution for FDI. Main steps in the design are: –Generate a set of potential residual generators. –Select the most suitable residual –Design the diagnostic tests for the selected set of residual generators are designed. A comparison between the estimated probability distributions of residuals is used for diagnostic tests and evaluated with current and no-fault data. 6 Svard, C. & Nyberg, M. Automated Design of an FDI-System for the Wind Turbine Benchmark Proceedings of IFAC World Congress 2011, 2011, 8307-8315

17. Results Simulation – Fault 1 Fault #GKSVEBUDCCOKGFM 1Td: Mean 0.02s, Min 0.02s, Max 0.02s Fd: Mean 0, Min 0, Max 0 Td: Mean 0.02s, Min 0.01s, Max 0.02s Fd: Mean 0, Min 0, Max 0 MD: Mean 3%, Min 0% Max 20% Td: Mean 0.03s, Min 0.02s, Max 0.03s Fd: Mean 0, Min 0, Max 0 Td: Mean 10.32s, Min 10.23s, Max 10.33s Fd: Mean 0.89, Min 0, Max 1 Td: Mean 0.04s, Min 0.03s, Max 0.04s Fd: Mean 0, Min 0, Max 0

18. Results Simulation – Fault 2 Fault #GKSVEBUDCCOKGFM 2Td: Mean 47.24s, Min 3.23s, Max 95.09s Fd: Mean 0, Min 0, Max 0 MD: Mean 56%, Min 0% Max 100% Td: Mean 44.65s, Min 0.63s, Max 95.82s Fd: Mean 22, Min 16, Max 28 MD: Mean 56%, Min 0% Max 100% Td: Mean 69.12s, Min 7.60s, Max 95.72s Fd: Mean 0, Min 0, Max 0 MD: Mean 67%, Min 0% Max 100% Td: Mean 19.24s, Min 3.43s, Max 49.93s Fd: Mean 0.97, Min 0, Max 5 Td: Mean 13.70s, Min 0.38s, Max 25.32s Fd: Mean 3.08, Min 1, Max 18

19. Results Simulation – Fault 3 Fault #GKSVEBUDCCOKGFM 3Td: Mean 0.02s, Min 0.02s, Max 0.02s Fd: Mean 0, Min 0, Max 0 Td: Mean 0.54s, Min 0.51s, Max 0.76s Fd: Mean 4, Min 1, Max 11 MD: Mean 3%, Min 0% Max 20% Td: Mean 0.04s, Min 0.03s, Max 0.10s Fd: Mean 0, Min 0, Max 0 MD: Mean 3%, Min 0% Max 20% Td: Mean 10.35s, Min 1.54s, Max 10.61s Fd: Mean 1.42, Min 1, Max 4 Td: Mean 0.05s, Min 0.03s, Max 0.06s Fd: Mean 1.61, Min 1, Max 5

20. Results Simulation – Fault 4 Fault #GKSVEBUDCCOKGFM 4Td: Mean 0.11s, Min 0.09s, Max 0.18s Fd: Mean 0, Min 0, Max 0 Td: Mean 0.33s, Min 0.27s, Max 0.44s Fd: Mean 0, Min 0, Max 0 Td: Mean 0.02s, Min 0.02s, Max 0.02s Fd: Mean 1, Min 1, Max 8 Td: Mean 0.18s, Min 0.03s, Max 0.46s Fd: Mean 2.31, Min 0, Max 5 Td: Mean 0.10s, Min 0.03s, Max 0.34s Fd: Mean 3.36, Min 1, Max 18

21. Results Simulation – Fault 5 Fault #GKSVEBUDCCOKGFM 5Td: Mean 25.90s, Min 1.24s, Max 87.49s Fd: Mean 0, Min 0, Max 0 MD: Mean 3%, Min 0% Max 20% Td: Mean 0.01s, Min 0.01s, Max 0.01s Fd: Mean 117, Min 95, Max 142 Td: Mean 2.96s, Min 0.38s, Max 21.08s Fd: Mean 0.75, Min 0, Max 3 Td: Mean 31.32s, Min 1.54s, Max 91.13s Fd: Mean 0.26, Min 0, Max 2 MD: Mean 14%, Min 0% Max 40% Td: Mean 9.49s, Min 0.56s, Max 17.18s Fd: Mean 2.42, Min 1, Max 18

22. Results Simulation – Fault 6 Fault #GKSVEBUDCCOKGFM 6MD: Mean 100%, Min 100% Max 100% Td: Mean 11.31s, Min 0.06s, Max 55.27s Fd: Mean 2, Min 0, Max 20 Td: Mean 11.81s, Min 0.53s, Max 55.72s Fd: Mean 22, Min 15, Max 25 Td: Mean 23.80s, Min 0.33s, Max 64.95s Fd: Mean 0.03, Min 0, Max 3 Td: Mean 15.52s, Min 0.02s, Max 61.13s Fd: Mean 3.67, Min 1, Max 37

23. Results Simulation – Fault 7 Fault #GKSVEBUDCCOKGFM 7MD: Mean 100%, Min 100% Max 100% Td: Mean 26.07s, Min 3.33s, Max 52.66s Fd: Mean 1.8, Min 1, Max 5 Td: Mean 12.93s, Min 2.86s, Max 51.08s Fd: Mean 2, Min 1, Max 4 Td: Mean 34.00s, Min 17.22s, Max 52.93s Fd: Mean 0, Min 0, Max 0 Td: Mean 31.70s, Min 0.61s, Max 180.70s Fd: Mean 1.25, Min 1, Max 5

24. Results Simulation – Fault 8 Fault #GKSVEBUDCCOKGFM 8Td: Mean 0.01s, Min 0.01s, Max 0.01s Fd: Mean 0, Min 0, Max 0 MD: Mean 97%, Min 0% Max 100% Td: Mean 0.01s, Min 0.01s, Max 0.01s Fd: Mean 0, Min 0, Max 0 MD: Mean 97%, Min 0% Max 100% Td: Mean 0.02s, Min 0.02s, Max 0.02s Fd: Mean 0, Min 0, Max 0 MD: Mean 97%, Min 0% Max 100% Td: Mean 0.01s, Min 0.01s, Max 0.01s Fd: Mean 0, Min 0, Max 0 MD: Mean 97%, Min 0% Max 100% Td: Mean 7.92s, Min 7.92s, Max 7.92s Fd: Mean 0, Min 0, Max 0 MD: Mean 97%, Min 0% Max 100%

25. Planned Continuations Competition Part II – FTC – 2 invited sessions proposals submitted to IFAC Safeprocess 2012 An extended version of this benchmark model by merging it with FAST. Planning a invited session on this for ACC 2013. Details and model available in primo 2012. With Kathryn Johnson Competition Part III (2013) & Part IV (2014) on a simple wind farm model with faults. Details and model available in primo 2012. With Jakob Stoustrup

26. Is the Objective Meet? Yes! –Higher than expected interest in the FDI and FTC parts of the competition. –General interest in the problem and benchmark model. We hope to continue the momentum of this interest into the new initiatives.