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Abstract and Applied Analysis
Volume 2014 (2014), Article ID 903493, 7 pages
http://dx.doi.org/10.1155/2014/903493
Research Article

Variable Torque Control of Offshore Wind Turbine on Spar Floating Platform Using Advanced RBF Neural Network

1Intelligent Systems and New Energy Technology Research Institute, Chongqing University, Chongqing 400044, China
2Institute of Intelligent System and Renewable Energy Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
3Web Science Center, University of Electronic Science and Technology of China, Chengdu 611731, China

Received 2 January 2014; Revised 14 January 2014; Accepted 15 January 2014; Published 6 March 2014

Academic Editor: Xiaojie Su

Copyright © 2014 Lei Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. F. G. Nielsen, T. D. Hanson, and B. Skaare, “Integrated dynamic analysis of floating offshore wind turbines,” in Proceedings of the 25TH International Conference on Offshore Mechanics and Arctic Engineering (OMAE '06), pp. 671–679, Hamburg, Germany, June 2006. View at Publisher · View at Google Scholar · View at Scopus
  2. W. E. Heronemus, “Pollution-free energy from offshore wind,” in Proceedings of the 8th Annual Conference and Exposition Marine Technology Society, Washington, DC, USA, 1972.
  3. W. Musial and S. Butterfield, “Future for offshore wind energy in the United States,” Tech. Rep. 36313, National Renewable Energy Laboratory, Golden, Colo, USA, 2004.
  4. J. M. Jonkman, “Dynamics modeling and loads analysis of an offshore floating wind turbine,” Tech. Rep. 41958, National Renewable Energy Laboratory, Golden, Colo, USA, 2007.
  5. J. M. Jonkman and D. Matha, “Dynamics of offshore floating wind turbines-analysis of three concepts,” Wind Energy, vol. 14, no. 4, pp. 557–569, 2011. View at Publisher · View at Google Scholar · View at Scopus
  6. S. Zuo, Y. D. Song, L. Wang, and Q.-W. Song, “Computationally inexpensive approach for pitch control of offshore wind turbine on barge floating platform,” The Scientific World Journal, vol. 2013, Article ID 357849, 9 pages, 2013. View at Publisher · View at Google Scholar
  7. W. Lei, Y.-L. He, X. Jin, J. Du, and S. Ma, “Dynamic simulation analysis of floating wind turbine,” Journal of Central South University: Science and Technology, vol. 43, no. 4, pp. 1309–1314, 2012.
  8. L. Wang, B. Wang, Y. Song, et al., “Fatigue loads alleviation of floating offshore wind turbine using individual pitch control,” Advances in Vibration Engineering, vol. 12, no. 4, pp. 377–390, 2013.
  9. H. Namik and K. Stol, “Individual blade pitch control of floating offshore wind turbines,” Wind Energy, vol. 13, no. 1, pp. 74–85, 2010. View at Publisher · View at Google Scholar · View at Scopus
  10. M. A. Lackner, “An investigation of variable power collective pitch control for load mitigation of floating offshore wind turbines,” Wind Energy, vol. 16, no. 3, pp. 435–444, 2012. View at Publisher · View at Google Scholar · View at Scopus
  11. Y. D. Song, “Control of wind turbines using memory-based method,” Journal of Wind Engineering and Industrial Aerodynamics, vol. 85, no. 3, pp. 263–275, 2000. View at Publisher · View at Google Scholar · View at Scopus
  12. Y. D. Song, B. Dhinakaran, and X. Bao, “Control of wind turbines using nonlinear adaptive field excitation algorithms,” in Proceedings of the IEEE American Control Conference, vol. 3, pp. 1551–1555, Chicago, Ill, USA, 2000. View at Publisher · View at Google Scholar
  13. L. Wu, W. X. Zheng, and H. Gao, “Dissipativity-based sliding mode control of switched stochastic systems,” IEEE Transactions on Automatic Control, vol. 58, no. 3, pp. 785–793, 2013. View at Publisher · View at Google Scholar
  14. J. F. Conroy and R. Watson, “Frequency response capability of full converter wind turbine generators in comparison to conventional generation,” IEEE Transactions on Power Systems, vol. 23, no. 2, pp. 649–656, 2008. View at Publisher · View at Google Scholar · View at Scopus
  15. J. Zaragoza, J. Pou, A. Arias, C. Spiteri, E. Robles, and S. Ceballos, “Study and experimental verification of control tuning strategies in a variable speed wind energy conversion system,” Renewable Energy, vol. 36, no. 5, pp. 1421–1430, 2011. View at Publisher · View at Google Scholar · View at Scopus
  16. J. Jonkman, S. Butterfield, W. Musial, and G. Scott, “Definition of a 5-MW reference wind turbine for offshore system development,” Tech. Rep. TP 500-38060, National Renewable Energy Laboratory, Golden, Colo, USA, 2009.
  17. J. M. Jonkman, “Influence of control on the pitch damping of a floating wind turbine,” in Proceedings of the 46th AIAA Aerospace Sciences Meeting and Exhibit, Reno, Nev, USA, January 2008. View at Scopus
  18. R. M. Sanner and J.-J. E. Slotine, “Gaussian networks for direct adaptive control,” IEEE Transactions on Neural Networks, vol. 3, no. 6, pp. 837–863, 1992. View at Publisher · View at Google Scholar · View at Scopus
  19. Y. Kourd, D. Lefebvre, and N. Guersi, “Fault diagnosis based on neural networks and decision trees: application to DAMADICS,” International Journal of Innovative Computing, Information and Control, vol. 9, no. 8, pp. 3185–3196, 2013.
  20. C. K. Ahn and M. K. Song, “New sets of criteria for exponential L2L stability of Takagi-Sugeno fuzzy systems combined with Hopfield neural networks,” International Journal of Innovative Computing, Information and Control, vol. 9, no. 7, pp. 2979–2986, 2013.
  21. S. Sefriti, J. Boumhidi, M. Benyakhlef, and I. Boumhidi, “Adaptive decentralized sliding mode neural network control of a class of nonlinear interconnected systems,” International Journal of Innovative Computing, Information and Control, vol. 9, no. 7, pp. 2941–2947, 2013.
  22. K. S. Narendra and K. Parthasarathy, “Identification and control of dynamical systems using neural networks,” IEEE Transactions on Neural Networks, vol. 1, no. 1, pp. 4–27, 1990. View at Publisher · View at Google Scholar · View at Scopus
  23. L. Wu, X. Su, P. Shi, and J. Qiu, “Model approximation for discrete-time state-delay systems in the T-S fuzzy framework,” IEEE Transactions on Fuzzy Systems, vol. 19, no. 2, pp. 366–378, 2011. View at Publisher · View at Google Scholar · View at Scopus
  24. X. Su, Z. Li, Y. Feng, and L. Wu, “New global exponential stability criteria for interval-delayed neural networks,” Journal of Systems and Control Engineering, vol. 225, Proceedings of the Institution of Mechanical Engineers, no. 1, pp. 125–136, 2011. View at Publisher · View at Google Scholar · View at Scopus
  25. X. Su, P. Shi, L. Wu, and Y.-D. Song, “A novel control design on discrete-time Takagi-Sugeno fuzzy systems with time-varying delays,” IEEE Transactions on Fuzzy Systems, vol. 20, no. 6, pp. 655–671, 2013.
  26. H. Li, K. L. Shi, and P. G. McLaren, “Neural-network-based sensorless maximum wind energy capture with compensated power coefficient,” IEEE Transactions on Industry Applications, vol. 41, no. 6, pp. 1548–1556, 2005. View at Publisher · View at Google Scholar · View at Scopus