Wind turbine power curve modeling using advanced parametric and nonparametric methods

IEEE Transactions on Sustainable Energy

Volumn 5, Issue 4, 2014, Pages 1262-1269

  Shokrzadeh, Shahab ^a   Jafari Jozani, Mohammad ^a   Bibeau, Eric ^a  

^a UNIVERSITY OF MANITOBA   (Canada)

Author keywords

Nonparametric regression; penalized spline regression; polynomial regression; wind energy; wind turbine power curve

Indexed keywords

NON-PARAMETRIC REGRESSION; NONPARAMETRIC METHODS; PENALIZED SPLINES; POLYNOMIAL REGRESSION; WIND TURBINE POWER;

WIND POWER;

EID: 84907425719     PISSN: 19493029     EISSN: None     Source Type: Journal    
DOI: 10.1109/TSTE.2014.2345059     Document Type: Article

References (37)

1. International Energy Agency (IEA), "World energy outlook-2013," IEA, Paris, Tech. Rep., 2013.
2. International Energy Agency (IEA), "Technology roadmap-Wind energy," IEA, Paris, Tech. Rep., 2013.
3. A. Ipakchi and F. Albuyeh, "Grid of the future," IEEE Power Energy Mag., vol. 7, no. 2, pp. 52-62, Mar. 2009.
4. A. Kusiak, A. Verma, and X. Wei, "Wind turbine capacity frontier from scada," Wind Syst. Mag., vol. 3, no. 9, pp. 36-39, 2012.
5. A. Benatiallah, L. Kadia, and B. Dakyob, "Modelling and optimisation of wind energy systems," Jordan J Mech. Ind. Eng., vol. 4, no. 1, 2010.
6. Z. Zhang, Q. Zhou, and A. Kusiak, "Optimization of wind power and its variability with a computational intelligence approach," IEEE Trans. Sustain. Energy, vol. 5, no. 1, pp. 228-236, Jan. 2014.
7. S. Shokrzadeh, M. Jafari-Jozani, E. Bibeau, and T. Molinski, "An energy storage sizing algorithm for base-load power generation of wind farms," submitted for publication.
8. R. Gasch and J. Twele, Wind Power Plants: Fundamentals, Design, Construction and Operation, 2nd ed. Berlin, Germany: Springer-Verlag, 2012.
9. J. F. Manwell, J. G. McGowan, and A. L. Rogers, Wind Energy Explained: Theory, Design and Application, 2nd ed. Hoboken, NJ, USA: Wiley, 2009.
10. C. Carrillo, A. O. Montao, J. Cidrs, and E. Daz-Dorado, "Review of power curve modelling for wind turbines," Renew. Sustain. Energy Rev., vol. 21, pp. 572-581, 2013.
11. M. Lydia, S. S. Kumar, A. I. Selvakumar, and G. E. P. Kumar, "A comprehensive review on wind turbine power curve modeling techniques," Renew. Sustain. Energy Rev., vol. 30, pp. 452-460, 2014.
12. A. Kusiak, H. Zheng, and Z. Song, "On-line monitoring of power curves," Renew. Energy, vol. 34, no. 6, pp. 1487-1493, 2009.
13. M. Hollander, D. Wolfe, and E. Chicken, Nonparametric Statistical Methods, 3rd ed. Hoboken, NJ, USA: Wiley, 2014.
14. M. G. Khalfallah and A. M. Koliub, "Suggestions for improving wind turbines power curves," Desalination, vol. 209, no. 3, pp. 221-229, 2007.
15. P. Giorsetto and K. F. Utsurogi, "Development of a new procedure for reliability modeling of wind turbine generators," IEEE Trans. Power App. Syst., vol. PAS-102, no. 1, pp. 134-143, Jan. 1983.
16. R. Chedid, H. Akiki, and S. Rahman, "A decision support technique for the design of hybrid solar-wind power systems," IEEE Trans. Energy Convers., vol. 13, no. 1, pp. 76-83, Mar. 1998.
17. M. Lydia, A. Selvakumar, S. Kumar, and G. Kumar, "Advanced algorithms for wind turbine power curve modeling," IEEE Trans. Sustain. Energy, vol. 4, no. 3, pp. 827-835, Jul. 2013.
18. A. Marvuglia and A. Messineo, "Monitoring of wind farms' power curves using machine learning techniques," Appl. Energy, vol. 98, no. 0, pp. 574-583, 2012.
19. T. Üstüntaş and A. D. Şahin, "Wind turbine power curve estimation based on cluster center fuzzy logic modeling," J Wind Eng. Ind. Aerodyn., vol. 96, no. 5, pp. 611-620, 2008.
20. A. Kusiak, H. Zheng, and Z. Song, "Models for monitoring wind farm power," Renew. Energy, vol. 34, no. 3, pp. 583-590, 2009.
21. T. J. Hastie and R. J. Tibshirani, Generalized Additive Models. London, U.K.: Chapman & Hall, 1990.
22. B. Ai, H. Yang, H. Shen, and X. Liao, "Computer-aided design of pv/wind hybrid system," Renew. Energy, vol. 28, no. 10, pp. 1491-1512, 2003.
23. S. Diaf, D. Diaf, M. Belhamel, M. Haddadi, and A. Louche, "A methodology for optimal sizing of autonomous hybrid pv/wind system," Energy Policy, vol. 35, no. 11, pp. 5708-5718, 2007.
24. J. Walker and N. Jenkins, Wind Energy Technology. Hoboken, NJ, USA: Wiley, 1997.
25. T. Ackermann and L. Sder, "Wind energy technology and current status: A review," Renew. Sustain. Energy Rev., vol. 4, no. 4, pp. 315-374, 2000.
26. T. Ackermann, Wind Power in Power Systems, 2nd ed. Hoboken, NJ, USA: Wiley, 2012.
27. A. Betz, "Das maximum der theoretisch moglichen ausnutzung des windes durch windmotoren, the maximum of the theoretically possible exploitation of the wind by wind motors," Z. Gesamte Turbinenwesen, vol. 26, pp. 307-309, 1920.
28. W. S. Cleveland and S. J. Devlin, "Locally weighted regression: An approach to regression analysis by local fitting," J Amer. Stat. Assoc., vol. 83, pp. 596-610, 1988.
29. W. S. Cleveland, "Robust locally weighted regression and smoothing scatterplots," J Amer. Stat. Assoc., vol. 74, pp. 829-836, 1979.
30. V. Thapar, G. Agnihotri, and V. K. Sethi, "Critical analysis of methods for mathematical modelling of wind turbines," Renew. Energy, vol. 36, no. 11, pp. 3166-3177, 2011.
31. G. Wahba, Spline Models for Observational Data. Philadelphia, PA: Society for Industrial and Applied Mathematics (SIAM), 1990.
32. T. J. Hastie, R. J. Tibshirani, and J. H. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, vol. 27, New York, NY, USA: Springer, 2005, pp. 83-85.
33. J. H. Friedman and B. W. Silverman, "Flexible parsimonious smoothing and additive modeling," Technometrics, vol. 31, no. 1, pp. 3-21, 1989.
34. D. Ruppert, "Selecting the number of knots for penalized splines," J Comput. Graph. Statist., vol. 11, no. 4, pp. 735-757, 2002.
35. S. Li, D. Wunsch, E. O'Hair, and M. Giesselmann, "Using neural networks to estimate wind turbine power generation," IEEE Trans. Energy Convers., vol. 16, no. 3, pp. 276-282, Sep. 2001.
36. Z. Liu, W. Gao, Y.-H. Wan, and E. Muljadi, "Wind power plant prediction by using neural networks," in 2012 IEEE Energy Convers. Congr. Expo. (ECCE), 2012, pp. 3154-3160.
37. A. Kusiak and X. Wei, "A data-driven model for maximization of methane production in a wastewater treatment plant." Water Sci. Technol., vol. 65, no. 6, 2012.