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Optimal Power Planning of Wind Turbines in a Wind Farm

Received: 5 December 2016     Accepted: 21 March 2017     Published: 14 April 2017
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Abstract

Wind energy is attractive in the presence of climate concerns and has the potential to dramatically reduce the dependency on nonrenewable energy resources. With the increase in wind farms there is a need to improve the efficiency in power allocation and power generation among wind turbines. In this paper, a hierarchical algorithm including a cooperative level and an individual level is developed for power coordination and planning in a wind farm. In the cooperative level, a constrained quadratic programming problem is formulated and solved to allocate the power to wind turbines considering the aerodynamic effects of wake interaction and the power generation capabilities of wind turbines. In the individual level, a method based on the local pursuit strategy is studied to connect the cooperative level power allocation and the individual level power generation using a virtual leader-follower scheme. The stability of individual wind turbine power generation is analyzed. Simulations are used to show the advantages of the method.

Published in American Journal of Electrical Power and Energy Systems (Volume 6, Issue 2)
DOI 10.11648/j.epes.20170602.11
Page(s) 7-15
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2017. Published by Science Publishing Group

Keywords

Wind Turbine, Coordinated Control, Wind Farm

References
[1] L. Y. Pao and K. E. Johnson, “Control of wind turbines: approaches, challenges, and recent developments,” IEEE Control Systems Magazine, vol. 31, no. 2, 2011, pp. 44-62.
[2] “Offshore wind energy,” http://www.boem.gov/renewable-energy-program/renewable-energy-guide/offshore-wind-energy.aspx.
[3] S. Schreck, J. Lundquist, and W. Shaw, “Research needs for wind resource characterization,” Bulletin of the American Meteorological Society, 2008, vol. 90, no. 4, pp. 535-538.
[4] K. E. Johnson, and N. Thomas, “Wind farm control: addressing the aerodynamic interaction among wind turbines,” in American Control Conf., 2009. ACC’09, pp. 2104-2109, Jun. 2009.
[5] J. Park, S. Kwon, and K. H. Law, “Wind farm power maximization based on a cooperative static game approach,” in Proc. SPIE Smart Structures/NDE Conf., vol. 8686, pp. 1-15, Mar. 2013.
[6] “Sandia Labs news releases, SWiFT commissioned to study wind farm optimization,” https://share.sandia.gov/news/resources/ news_releases/swift-wind-farm-optimization/#. UzebrfldWSp.
[7] Z. Wang, C. Cai, and K. Jia, “Neural Network adaptive control for constant output power of variable pitch wind turbine,” in IEEE Int. Conf. on Vehicular Electronics and Safety, IEEE, Dongguan, China, pp. 165-170, July 2013.
[8] G. Semrau, S. Rimkus, and T. Das, “Nonlinear systems analysis and control of variable speed wind turbines for multiregime operation,” ASME Journal of Dynamic Systems, Measurement, and Control, 2015, vol. 137, no. 4, pp. 041007-1 – 041007-10.
[9] V. Spudic, M. Baotic, and N. Peric, “Wind farm load reduction via parametric programming based controller design,” in Proc. 18th IFAC World Congress, Milano, Italy, pp. 1704-1709, Aug. 2011.
[10] T. Knudsen, T. Bak, and M. Svenstrup, “Survey of wind farm control – power and fatigue optimization,” in Wind Energy 2015, Wiley Online Library, John Wiley & Sons Ltd., pp. 1333-1351, May, 2014.
[11] D. Madjidian, M. Kristalny, A. Rantzer, “Dynamic power coordination for load reduction in dispatchable wind power plants,” in 2013 European Control Conf., Zurich, Switzerland, pp. 3554-3559, July, 2013.
[12] L. Munteanu, N. A. Cutululis, A. I. Bratchu, and E. Ceanga, “Optimization of variable speed wind power systems based on a LQG approach,” Control Engineering Practice, vol. 13, no. 7, pp. 903-912, July 2005.
[13] V. Spudic, M. Jelavic, M. Baotic, and N. Peric, “Hierarchical wind farm control for power/load optimization,” in Proc. Conf. Sci. Making Torque from Wind, 2010, pp. 681-692.
[14] M. Soleimanzadeh, A. J. Brand, and R. Wisniewski, “A wind farm controller for load and power optimization in a farm,” in 2011 IEEE Int. Sym. Computer-Aided Control Systems Design, Denver, CO, pp. 1202-1207, Sep. 2011.
[15] Y. Xu, C. Remeikas, and K. Pham, “Local pursuit strategy inspired cooperative trajectory planning algorithm for a class of nonlinear constrained dynamical systems,” International Journal of Control, vol. 87, no. 3, 2013, pp. 506-523.
[16] D. Hristu-Varsakelis, and C. Shao, “Biologically-inspired optimal control: learning from social insects,” International Journal of Control, 2004, vol. 77, no. 18, pp.1549-1566.
[17] J. Jonkman, S. Butterfield, W. Musial, and G. Scott, “Definition of a 5-MW reference wind turbine for offshore system development,” Technical Report from National Renewable Energy Lab., U. S. Dept. of Energy, Feb. 2009.
[18] J. Hui and A. Bakhshai, “A new adaptive control algorithm for maximum power point tracking for wind energy conversion systems,” in Proc. IEEE Power Electronics Specialists Conf., 2008, pp. 4003-4007.
[19] D. J. Renkema, “Validation of wind turbine wake models: using wind farm data and wind tunnel measurements,” M. S. Thesis, Delft, Univ. of Technology, 2007.
[20] V. Spudic, M. Jelavic, M. Baotic, and M. Vasak, “Distributed control of large-scale offshore wind farms,” AEOLUS Technical Report, Univ. of Zagreb, 2008.
[21] J. J. Slotine and W. Li, Applied Nonlinear Control, Prentice Hall, Englewood Cliffs, NJ, 1991.
Cite This Article
  • APA Style

    Puneet Vishwakarma, Yunjun Xu, Kuo-Chi Lin. (2017). Optimal Power Planning of Wind Turbines in a Wind Farm. American Journal of Electrical Power and Energy Systems, 6(2), 7-15. https://doi.org/10.11648/j.epes.20170602.11

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    ACS Style

    Puneet Vishwakarma; Yunjun Xu; Kuo-Chi Lin. Optimal Power Planning of Wind Turbines in a Wind Farm. Am. J. Electr. Power Energy Syst. 2017, 6(2), 7-15. doi: 10.11648/j.epes.20170602.11

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    AMA Style

    Puneet Vishwakarma, Yunjun Xu, Kuo-Chi Lin. Optimal Power Planning of Wind Turbines in a Wind Farm. Am J Electr Power Energy Syst. 2017;6(2):7-15. doi: 10.11648/j.epes.20170602.11

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  • @article{10.11648/j.epes.20170602.11,
      author = {Puneet Vishwakarma and Yunjun Xu and Kuo-Chi Lin},
      title = {Optimal Power Planning of Wind Turbines in a Wind Farm},
      journal = {American Journal of Electrical Power and Energy Systems},
      volume = {6},
      number = {2},
      pages = {7-15},
      doi = {10.11648/j.epes.20170602.11},
      url = {https://doi.org/10.11648/j.epes.20170602.11},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.epes.20170602.11},
      abstract = {Wind energy is attractive in the presence of climate concerns and has the potential to dramatically reduce the dependency on nonrenewable energy resources. With the increase in wind farms there is a need to improve the efficiency in power allocation and power generation among wind turbines. In this paper, a hierarchical algorithm including a cooperative level and an individual level is developed for power coordination and planning in a wind farm. In the cooperative level, a constrained quadratic programming problem is formulated and solved to allocate the power to wind turbines considering the aerodynamic effects of wake interaction and the power generation capabilities of wind turbines. In the individual level, a method based on the local pursuit strategy is studied to connect the cooperative level power allocation and the individual level power generation using a virtual leader-follower scheme. The stability of individual wind turbine power generation is analyzed. Simulations are used to show the advantages of the method.},
     year = {2017}
    }
    

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    T1  - Optimal Power Planning of Wind Turbines in a Wind Farm
    AU  - Puneet Vishwakarma
    AU  - Yunjun Xu
    AU  - Kuo-Chi Lin
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    N1  - https://doi.org/10.11648/j.epes.20170602.11
    DO  - 10.11648/j.epes.20170602.11
    T2  - American Journal of Electrical Power and Energy Systems
    JF  - American Journal of Electrical Power and Energy Systems
    JO  - American Journal of Electrical Power and Energy Systems
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    EP  - 15
    PB  - Science Publishing Group
    SN  - 2326-9200
    UR  - https://doi.org/10.11648/j.epes.20170602.11
    AB  - Wind energy is attractive in the presence of climate concerns and has the potential to dramatically reduce the dependency on nonrenewable energy resources. With the increase in wind farms there is a need to improve the efficiency in power allocation and power generation among wind turbines. In this paper, a hierarchical algorithm including a cooperative level and an individual level is developed for power coordination and planning in a wind farm. In the cooperative level, a constrained quadratic programming problem is formulated and solved to allocate the power to wind turbines considering the aerodynamic effects of wake interaction and the power generation capabilities of wind turbines. In the individual level, a method based on the local pursuit strategy is studied to connect the cooperative level power allocation and the individual level power generation using a virtual leader-follower scheme. The stability of individual wind turbine power generation is analyzed. Simulations are used to show the advantages of the method.
    VL  - 6
    IS  - 2
    ER  - 

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Author Information
  • Mechanical and Aerospace Engineering Department, University of Central Florida, Orlando, USA

  • Mechanical and Aerospace Engineering Department, University of Central Florida, Orlando, USA

  • Mechanical and Aerospace Engineering Department, University of Central Florida, Orlando, USA

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