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JACIII Vol.22 No.1 pp. 76-87
doi: 10.20965/jaciii.2018.p0076
(2018)

Paper:

Power Curve Based-Fuzzy Wind Speed Estimation in Wind Energy Conversion Systems

Agus Naba and Ahmad Nadhir

Study Program of Instrumentation, Department of Physics, Faculty of Mathematics and Natural Sciences, University of Brawijaya
Jl. Veteran, Malang, East Java 65145, Indonesia

Received:
December 26, 2016
Accepted:
October 11, 2017
Published:
January 20, 2018
Keywords:
sensorless wind speed estimation, fuzzy logic principles, wind power, wind energy conversion system, wind turbines
Abstract

Availability of wind speed information is of great importance for maximization of wind energy extraction in wind energy conversion systems. The wind speed is commonly obtained from a direct measurement employing a number of anemometers installed surrounding the wind turbine. In this paper a sensorless fuzzy wind speed estimator is proposed. The estimator is easy to build without any training or optimization. It works based on the fuzzy logic principles heuristically inferred from the typical wind turbine power curve. The wind speed estimation using the proposed estimator was simulated during the operation of a squirrel-cage induction generator-based wind energy conversion system. The performance of the proposed estimator was verified by the well estimated wind speed obtained under the wind speed variation.

References
  1. [1] M. Abdullah, A. Yatim, C. Tan, and R. Saidur, “A review of maximum power point tracking algorithms for wind energy systems,” Renewable and Sustainable Energy Reviews, Vol.16, pp. 3220-3227, 2012.
  2. [2] H. Li, K. L. Shi, and P. McLaren, “Neural-network-based sensorless maximum wind energy capture with compensated power coefficient,” IEEE Trans. Ind. Appl., Vol.41, Issue 6, pp. 1548-1156, 2005.
  3. [3] A. Mesemanolis and C. Mademlis, “A Neural Network Based MPPT Controller for Variable Speed Wind Energy Conversion Systems,” 8th Mediterranean Conf. on Power Generation, Transmission, Distribution and Energy Conversion (MEDPOWER 2012), 2012.
  4. [4] M. Monfared, H. Rastegar, and H. M. Kojabadi, “A new strategy for wind speed forecasting using artificial intelligent methods,” Renewable Energy, Vol.34, pp. 845-848, 2009.
  5. [5] A. Naba, A. Nadhir, and T. Hiyama, “Fuzzy model power curve based linear controller for maximum wind energy extraction in variable speed wind energy conversion system,” The 2011 IFSA World Congress and the 2011 AFSS, 2011.
  6. [6] A. Nadhir, A. Naba, and T. Hiyama, “FIS/ANFIS Based Optimal Control for Maximum Power Extraction in Variable-speed Wind Energy Conversion System,” IEEJ Trans. on Power and Energy, Vol.131, No.8, pp. 708-714, 2011.
  7. [7] A. G. Abo-Khalil and D. C. Lee, “MPPT control of wind generation systems based on estimated wind speed using SVR,” IEEE Trans. Ind. Appl., Vol.55, Issue 3, pp. 1489-1490, 2008.
  8. [8] M. Azzouz, A. l. Elshafei, and H. Emara, “Evaluation of fuzzy-based maximum power-tracking in wind energy conversion systems,” IET Renewable Power Generation, Vol.5, Issue 6, pp. 422-430, 2011.
  9. [9] R. Rocha, “A sensorless control for a variable speed wind turbine operating at partial load,” Renewable Energy, Vol.36, pp. 132-141, 2011.
  10. [10] J. Brahmi, L. Krichen, and A. Ouali, “A comparative study between three sensorless control strategies for PMSG in wind energy conversion system,” Applied Energy, Vol.86, Issue 9, pp. 1565-1573, 2009.
  11. [11] M. Pucci and M. Cirrincione, “Neural MPPT Control of Wind Generators With Induction Machines Without Speed Sensors,” IEEE Trans. on Industrial Electronics, Vol.58, Issue 1, pp. 37-47, 2011.
  12. [12] W. Qiao, W. Zhou, J. M. Aller, and R. G. Harley, “Wind speed estimation based sensorless output maximization control for a wind turbine driving a dfig,” IEEE Trans. on Power Electronics, Vol.23, pp. 1156-1169, 2008.
  13. [13] V. Calderaro, V. Galdi, A. Piccolo, and P. Siano, “A fuzzy controller for maximum energy extraction from variable speed wind power generation systems,” Electric Power Systems Research, Vol.78, pp. 1109-1118, 2008.
  14. [14] V. Galdi, A. Piccolo, and P. Siano, “Designing an Adaptive Fuzzy Controller for Maximum Wind Energy Extraction,” IEEE Trans. On Energy Conversion, Vol.23, Issue 2, pp. 559-569, 2008.
  15. [15] V. Galdi, A. Piccolo, and P. Siano, “Exploiting maximum energy from variable speed wind power generation systems by using an adaptive Takagi–Sugeno–Kang fuzzy model,” Energy Conversion and Management, Vol.50, pp. 413-421, 2009.
  16. [16] A. Naba, “Fuzzy Logic Principles for Wind Speed Estimation in Wind Energy Conversion Systems,” Int. Conf. on Information Technology and Electrical Engineering (ICITEE) 2014, 2014.
  17. [17] I. Munteanu, A. I. Bratcu, N. Cutululis, and E. Ceanga, “MATLAB®Case-study Simulations,” http://www.springer.com/gp/book/9781848000797 [accessed August 26, 2015]
  18. [18] I. Munteanu, A. I. Bratcu, N.-A. Cutululis, and E. Ceang, “Optimal Control of Wind Energy Systems, Towards a Global Approach,” Advances in Industrial Control, Springer-Verlag London Ltd., 2008.
  19. [19] L.-X. Wang, “A course in fuzzy systems and control,” Prentice-Hall International, Inc., 1997.
Cite this article as:
Agus Naba and Ahmad Nadhir, “Power Curve Based-Fuzzy Wind Speed Estimation in Wind Energy Conversion Systems,” J. Adv. Comput. Intell. Intell. Inform., Vol.22, No.1, pp. 76-87, 2018
Agus Naba and Ahmad Nadhir, J. Adv. Comput. Intell. Intell. Inform., Vol.22, No.1, pp. 76-87, 2018

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Last updated on Jun. 20, 2018