Global data-driven modeling of wind turbines in the presence of turbulence

Control Engineering Practice

Volumn 21, Issue 4, 2013, Pages 441-454

  van der Veen, G J ^a   van Wingerden, J W ^a   Fleming, P A ^b   Scholbrock, A K ^b   Verhaegen, M ^a  

^a DELFT UNIVERSITY OF TECHNOLOGY   (Netherlands)

^b NATIONAL RENEWABLE ENERGY LABORATORY   (United States)

Author keywords

Closed loop system identification; Data driven modeling; Hammerstein systems; Turbulence; Wind turbines

Indexed keywords

CLOSED-LOOP CONTROL; CLOSED-LOOP SUBSPACE IDENTIFICATIONS; DATA-DRIVEN MODELING; GLOBAL MODELS; HAMMERSTEIN SYSTEM; LINEAR DYNAMICS; LINEAR TIME-INVARIANT; MEAN WIND SPEED; MULTI-INPUT MULTI-OUTPUT; NONLINEAR STATICS; OPERATING CONDITION; OPERATIONAL DATA; TURBULENT WIND FIELDS; DATA-DRIVEN MODEL;

CLOSED LOOP SYSTEMS; CONTROL NONLINEARITIES; DIGITAL STORAGE; DYNAMICS; IDENTIFICATION (CONTROL SYSTEMS); TURBULENCE;

WIND TURBINES;

EID: 84873724101     PISSN: 09670661     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.conengprac.2012.12.008     Document Type: Article

References (57)

1. van Baars, G. E., & Bongers, P. M. M. (1994). Closed loop system identification of an industrial wind turbine system: experiment design and first validation results. In Proceedings of the 33rd IEEE conference on decision and control (pp. 625-630, Vol. 1).
2. van Baars, G. E., Mosterd, H., & Bongers, P. M. M. (1993). Extension to standard system identification of detailed dynamics of a flexible wind turbine system. In Proceedings of the 32nd IEEE conference on decision and control (pp. 3514-3519, Vol. 4).
3. Balini, H., Houtzager, I., Witte, J., & Scherer, C. W. (2010). Subspace identification and robust control of an AMB system. In American control conference 2010.
4. Bernhammer, L. O., De Breuker, R., Karpel, M., & van der Veen, G. J. Aeroelastic control using distributed floating flaps activated by piezoelectic tabs. AIAA Journal of Aircraft, in press.
5. Bianchi F.D., De Battista H., Mantz R.J. Wind turbine control systems: Principles, modelling and gain scheduling design 2007, Springer-Verlag, London.
6. Bongers, P. M. M. (1994). Modeling and identification of flexible wind turbines and a factorizational approach to robust control. Ph.D. Thesis. Delft University of Technology.
7. Bossanyi E.A. The design of closed loop controllers for wind turbines. Wind Energy 2000, 3:149-163.
8. Bossanyi E.A. Wind turbine control for load reduction. Wind Energy 2003, 6:229-244.
9. Bossanyi, E. A., Fleming, P. A., & Wright, A. D. (2012). Field test results with individual pitch control on the NREL CART3 wind turbine. In 50th AIAA aerospace sciences meeting including the new horizons forum and aerospace exposition, Nashville, TN.
10. Bossanyi E.A., Savini B., Iribas M., Hau M., Fischer B., Schlipf D., et al. Advanced controller research for multi-MW wind turbines in the UPWIND project. Wind Energy 2012, 15:119-145.
11. Brewer J.W. Kronecker products and matrix calculus in system theory. IEEE Transactions on Circuits and Systems 1978, 25:772-781.
12. Buhl, M. L. (2012). NWTC Design Codes (WT_Perf). Last accessed 16 May 2012.
13. Burton T., Sharpe D., Jenkins N., Bossanyi E. Wind energy handbook 2001, John Wiley & Sons.
14. Chiuso A. On the relation between CCA and predictor based subspace identification. IEEE Transactions on Automatic Control 2007, 52:1795-1812.
15. Chou C.T., Verhaegen M. Subspace algorithms for the identification of multivariable dynamic errors-in-variables models. Automatica 1997, 33:1857-1869.
16. Fleming, P. A., Wright, A. D., Fingersh, L. J., & van Wingerden, J. W. (2011). Resonant vibrations resulting from the re-engineering of a constant-speed 2-bladed turbine to a variable-speed 3-bladed turbine. In 49th AIAA aerospace sciences meeting.
17. Golub G.H., Van Loan C.F. Matrix computations 1996, The Johns Hopkins University Press. 3rd ed.
18. Gottschall J., Peinke J. How to improve the estimation of power curves for wind turbines. Environmental Research Letters 2008, 3.
19. Hansen M.H., Thomsen K., Fuglsang P., Knudsen T. Two methods for estimating aeroelastic damping of operational wind turbine modes from experiments. Wind Energy 2006, 9:179-191.
20. Henriksen, L., Hansen, M., & Poulsen, N. A simplified dynamic inflow model and its effect on the performance of free mean wind speed estimation. Wind Energy, n/a-n/a, DOI: , in press. http://dx.doi.org/10.1002/we.1548.
21. Hermans L., Van der Auweraer H. Modal testing and analysis of structures under operational conditions. Industrial applications. Mechanical Systems and Signal Processing 1999, 13:193-216.
22. Houtzager, I., Kulcsár, B., van Wingerden, J. W., & Verhaegen, M. (2010). System identification for the control of wind turbine systems. In Proceedings of the third conference, The Science of Making Torque from Wind, Heraklion, Crete, Greece.
23. IEC 61400 (2005). Wind turbines-design requirements. International Electrotechnical Commission.
24. Iribas-Latour M., Landau I.-D. Identification of wind turbines in closed-loop operation in the presence of three-dimensional turbulence wind speed. Torque demand to measured generator speed loop. Wind Energy 2009, 12:660-675.
25. Iribas-Latour, M., & Landau, I.-D. (2012). Identification in closed-loop operation of models for collective pitch robust controller design. Wind Energy (Online pre-print), N/A.
26. James III, G. H., Carne, T. G., & Lauffer, J. P. (1993). The natural excitation technique (NExT) for modal parameter extraction from operating wind turbines. Technical Report Sandia.
27. Jelavic, M., Peric, N., & Petrovic, I. (2006). Identification of wind turbine model for controller design. In: Power electronics and motion control conference. EPE-PEMC 2006. 12th International (pp. 1608-1613).
28. Johnson, K. E., Fingersh, L. J., Balas, M. J., & Pao, L. Y. (2004). Methods for increasing region 2 power capture on a variable speed HAWT. In: 42nd AIAA aerospace sciences meeting and exhibit, Reno, Nevada.
29. Jonkman, J. (2012). NWTC Design Codes (FAST). Last accessed 16 May 2012.
30. Knudsen, T., Andersen, P., & Toffner-Clausen, S. (1997). Comparing PI and robust pitch controller on a 400kW wind turbine by full scale tests. In Proceedings of the European wind energy conference (EWEC). Dublin, Ireland.
31. Knudsen T., Bak T., Soltani M. Prediction models for wind speed at turbine locations in a wind farm. Wind Energy 2011, 14:877-894.
32. Laks, J. H., Pao, L. Y., & Wright, A. D. (2009). Control of wind turbines: Past, present, and future. In Proceedings of the American control conference, 2009. ACC '09 (pp. 2096-2103).
33. Leithead, W. E., & Dominguez, S. (2006). Coordinated control design for wind turbine control systems. In EWEC Athens, Greece.
34. Ljung L. System identification. Theory for the user 1998, Prentice-Hall, PTR. 2nd ed.
35. Manwell J., McGowan J., Rogers A. Wind energy explained: theory, design and application 2002, John Wiley and Sons Ltd., Chichester.
36. Marrant, B. A. H., & van Holten, Th. (2004). System identification for the analysis of aeroelastic stability of wind turbine blades. In Proceedings of the European wind energy conference (EWEC). London, England.
37. Osgood, R., Bir, G., Mutha, H., Peeters, B., Luczak, M., & Sablon, G. (2011). Full-scale modal wind turbine tests: comparing shaker excitation with wind excitation. In: Proulx, T. (Ed.), Structural dynamics and renewable energy (Vol. 1), Springer, New York. Conference proceedings of the society for experimental mechanics series (pp. 113-124, Vol. 10).
38. Østergaard K.Z., Brath P., Stoustrup J. Estimation of effective wind speed. Journal of Physics, Conference Series. The Science of Making Torque from Wind 2007, 75:5-11.
39. Özbek, M., & Rixen, D. J. Operational modal analysis of a 2.5MW wind turbine using optical measurement techniques and strain gauges. Wind Energy, DOI: , in press. http://dx.doi.org/10.1002/we.1493.
40. Qin, S. J., & Ljung, L. (2003). Closed-loop subspace identification with innovation estimation. In IFAC symposium on system identification, August 2003, Rotterdam.
41. Simley, E., Pao, L. Y., Kelley, N., Jonkman, B., & Frehlich, R. (2012). LIDAR wind speed measurements of evolving wind fields. In 50th AIAA aerospace sciences meeting.
42. Skjoldan P.F., Hansen M.H. Implicit Floquet analysis of wind turbines using tangent matrices of a non-linear aeroelastic code. Wind Energy 2012, 15:275-287.
43. Smith D.A., Harris M., Coffey A.S., Mikkelsen T., Jørgensen H.E., Mann J., et al. Wind lidar evaluation at the Danish wind test site in Høvsøre. Wind Energy 2006, 9:87-93.
44. Snel, H., & Schepers, J. (1995). Joint investigation of dynamic inflow effects and implementation of an engineering method. Technical Report Energy Research Centre of the Netherlands.
45. van der Veen, G. J., Couchman, I. A., & Bowyer, R. O. (2012a). Control of floating wind turbines. In Proceedings of the 2012 American control conference, Montreal, QC, Canada.
46. van der Veen, G. J., van Wingerden, J. W., Bergamasco, M., Lovera, M., & Verhaegen, M. Closed-loop subspace identification methods: An overview. IET control theory and applications, submitted for publication.
47. van der Veen, G. J., van Wingerden, J. W., & Verhaegen, M. (2010a). Closed-loop MOESP subspace model identification with parametrizable disturbances. In Proceedings of the 49th IEEE control conference on decision and control. Atlanta.
48. van der Veen, G. J., van Wingerden, J. W., & Verhaegen, M. (2010b). Closed-loop system identification of wind turbines in the presence of periodic effects. In Proceedings of the third conference, the science of making torque from wind, Heraklion, Crete, Greece.
49. van der Veen, G. J., van Wingerden, J. W., & Verhaegen, M. (2011). Data-driven modelling of wind turbines. In Proceedings of the American control conference (ACC) (pp. 72-77). San Francisco.
50. van der Veen, G. J., van Wingerden, J. W., & Verhaegen, M. (2012). Global identification of wind turbines using a Hammerstein identification method. IEEE Transactions on Control Systems Technology.
51. Verhaegen M. Subspace model identification. Part 3: Analysis of the ordinary output-error state-space model identification algorithm. International Journal of Control 1993, 58:555-586.
52. Verhaegen M., Verdult V. Filtering and system identification: A least squares approach 2007, Cambridge University Press, Cambridge, UK. 1st ed.
53. de Visser, C. C. (2011). Global nonlinear model identification with multivariate splines. Ph.D. Thesis. Delft University of Technology.
54. de Visser, C. C., Chu, Q. P., & Mulder, J. A. (2009). A new approach to linear regression with multivariate splines. Automatica, 2903-2909.
55. de Visser C.C., Chu Q.P., Mulder J.A. Differential constraints for bounded recursive identification with multivariate splines. Automatica 2011, 47:2059-2066.
56. van Wingerden J.W., Hulskamp A.W., Barlas T., Houtzager I., Bersee H., van Kuik G.A.M., et al. Two-degree-of-freedom active vibration control of a prototyped "smart" rotor. IEEE Transactions on Control Systems Technology 2011, 19:284-296.
57. van Wingerden J.W., Verhaegen M. Subspace identification of bilinear and LPV systems for open- and closed-loop data. Automatica 2009, 45:372-381.