Document Type : Research Paper


1 Department of Electrical Engineering and Information Technology, Iranian Research Organization for Science and Technology

2 Department of Mechanical Engineering, Hakim Sabzevari university


This paper describes a new method for harvesting maximum electrical energy in wind farms. In proposing technique, the stochastic process principles are applied for detecting fault measurements of sensors. On the other hand, the wind farm is modeled by using fuzzy concept. Thereby the turbines are controlled against continuous changes in speed, direction and eddy currents of the blowing wind. To evaluate the performance of the proposed method three practical conditions of wind blowing are simulated. In the first scenario, the normal wind is simulated with low turbulence and slow changes. The second scenario belongs to high turbulence winds with sudden shifts in their parameters, and finally in the most complex scenario, several eddy currents are considered in blowing winds too. The obtained results show that the proposed method provides greater and more uniform harvested power compared to alternative methods. Furthermore, its superiority against other techniques has increased in parallel with the scenario become more complicated.

Graphical Abstract

A new strategy for controlling wind turbines against sensor faults and wake effects to harvest more electrical energy


Main Subjects

[1]        Godfrey, Boyle, Solar photovoltaics., Third edition, Oxford University Press, Oxford,  pp. 66-104, (2004).

[2]     O. Anaya-Lara, N. Jenkins, J. Ekanayake, P. Cartwright, M. Hughes, Wind energy generation: modelling and control., John Wiley & Sons; (2009).

[3]     Worldwide electricity production from renewable energy sources, online:

[4]     W. Ying, J. Lin, Y. Jiang, and T. Zheng,  “Study for Comprehensive Regulation of the Frequency Characteristics of Doubly-Fed Variable Speed Wind Turbine,” Advanced Materials Research, Vol. 403, Chapter 14,  pp.4024-29, (2012).

[5]     H. Kurt, R. Barthelmie, L. Jensen, and A. Sommer, “The impact of turbulence intensity and atmospheric stability on power deficits due to wind turbine wakes at Horns Rev wind farm,” Wind Energy, VOL 15, No 1, pp. 183-196, (2012).

[6]     S. Chowdhury, J. Zhang, A. Messac,  and L. Castillo, “Unrestricted wind farm layout optimization (UWFLO): Investigating key factors influencing the maximum power generation,” Renewable Energy, Vol. 38, No. 1, pp. 16-30, (2012).

[7]     A. Yassine, M. Hachemi, E. Al-Ahmar,  Bensaker, B., and Turri, S., “A brief status on condition monitoring and fault diagnosis in wind energy conversion systems,” Renewable and Sustainable Energy Reviews, Vol. 13, No. 9, pp. 2629-36, (2009).

[8]     Z. Hameed, Y. Hong, Y. Cho, S. Ahn,  and C. Song, “Condition monitoring and fault detection of wind turbines and related algorithms: A review,” Renewable and Sustainable Energy Reviews, Vol. 13, No. 1, pp. 1-39, (2009).

[9]     A. Kusiak, and L. Wenyan, “The prediction and diagnosis of wind turbine faults,” Renewable Energy, Vol. 36, No. 1, pp. 16-23, (2011).

[10]   S. Li, T. Haskew, K. Williams, and R. Swatloski, “Control of DFIG Wind Turbine With Direct-Current Vector Control Configuration,” IEEE Transactions on Sustainable Energy, Vol. 31, No. 1, pp. 1-11, (2012).

[11]   N. Karakasis, A. Mesemanolis, C. Mademlis, “Performance study of start-up control techniques in a Wind Energy Conversion System with induction generator,”International Symposium on Power Electronics, Electrical Drives, Automation and Motion (SPEEDAM),Sorrento, Italy, Jun,No. 6, pp. 547-552, (2012).

[12]   P. Novak, T. Ekelund, I. Jovilk, and B. Schmidtbauer, “Modeling and control of variable speed wind turbine drive systems dynamics,” IEEE Control Systems Magazine, Vol. 15, No. 4, pp. 28-38, (1995).

 [13] K. Johnson, Y. Pao, M. Balas, and L. Fingersh, “Control of variable-speed wind turbines: standard and adaptive techniques for maximizing energy capture,” IEEE Control Systems Magazine, Vol. 26, No. 3, pp. 70-81, (2006).

 [14]  H. Zhihong, Z. Yuan, and X. Chang, “State estimation for wind turbine system      based     on    Kalman    filter,

          ”International symposium on Systems and Control in Aerospace and stronautics, Shenzhen, China, Vol. 2, No. 12, December, pp. 1-3, (2008).

[15] O. Barambones, “A robust wind turbine control using a Neural Network based wind speed estimator,” International

Joint Conference on Neural Networks (IJCNN), Barcelona, Spain, Vol. 3, pp. 1-8, (2010).

[16] M. Degroot, and M. Schervish, Probability and Statistics., Addison-Wesley, Boston, USA, (2002).

[17] I. Myung, “Tutorial on maximum likelihood estimation,” Journal of Mathematical Psychology., Vol. 47, No. 1, pp. 90-100, (2003).

[18]   S. Al-Sharhan, “Fuzzy entropy: a brief survey,” IEEE International Conference on Fuzzy Systems, Vol. 3, Melbourne, Australia, pp. 1135-39, (2001)

[19]   S. Xuan, W. Xiaoye, W. Zhou, and X. Ying, “A new fuzzy clustering algorithm based on entropy weighting,” Journal of Computational Information Systems, Vol. 6, No. 10, pp. 3319-26, (2010).

[20]   M. Brown, and C. Harris, Neuro-Fuzzy Adaptive Modeling and Control, Prentice Hall, New York, USA, (1994).

[21]   J. Jang, “ANFIS: Adaptive-Network-based fuzzy inference system,” IEEE Transactions on systems, Manufacturing and Cybernetics, Vol. 23, No. 3, pp. 665-685, (1993).

[22] Hamed Badihi, Zhang, Youmin and  Hong. Henry "Fuzzy gain-scheduled active fault-tolerant control of a wind turbine." Journal of the Franklin Institute., Vol. 351, No. 7,  pp. 3677-3706, (2014).

[23]   Y. Ren, and G. Bao, “Control strategy of maximum wind energy capture of direct-drive wind turbine generator based on neural-network,” Proceedings of Power and Energy Engineering Conference, Chengdu, China, March, pp. 1-4, (2010).