BibTex RIS Kaynak Göster

Wind Energy Conversion Systems Control Using Inverse Neural Model Algorithm

Yıl 2010, Cilt: 2 Sayı: 3, 40 - 46, 01.09.2010

Öz

In this paper, a neural inverse model controller to achieve maximum power tracking for wind energy conversion systems (WECS's) employing a double- fed induction generator (DFIG) is proposed. Changes on the firing angle of the inverter can control the operation point of the generator. This purpose complies with a neural network (NN) controller. Its feasibility and effectiveness are demonstrated by simulation results of a typical turbine/generator pair

Kaynakça

  • [1] F. D. Bianchi, H. De Battista and R. J. Mantz, Wind Turbine Control Systems Principles, Modelling and Gain Scheduling Design .Springer-Verlag London Limited 2007.
  • [2] M. N. Eskander, “Neural network Controller for a permanent magnet generator applied n a wind energy conversion systems,” 2002.
  • [3] M.Sedighizadeh and A.Rezazadeh, “Adaptive PID control of wind energy conversion systems using RASPI mother wavelet basis function Networks,” Proceeding of World Academy of Science, Engineering and Technology, vol. 27, February 2008.
  • [4] M.Sedighizadeh and A.Rezazadeh, “Adaptive PID controller based on reinforcement learning for wind turbine control,” Proceeding of World Academy of Science, Engineering and Technology, vol. 27, February 2008.
  • [5] M. Bayat, H. K. Karegar, “Application of Predictive Control in DFIG Wind Turbines” unpublished.
  • [6] P. Simoes, B. K. Bose, and R. J. Spiegel, “Fuzzy logic-based intelligent control of a variable speed cage machine wind generation system,” IEEE Trans. Power Electron., vol. 12, no. 1, Jan. 1997.
  • [7] Z. Chen and S. A. Gomez and M. McCormick, “A Fuzzy logic controlled power electronic systems for variable speed wind energy conversion systems,”.
  • [8] K. Narendra and K. Parthasarathy, “Identification and control of dynamical systems using neural networks,” IEEE Trans. Neural Networks, vol. 1, Mar. 1990.
  • [9] S. Haykin, Neural Networks, a Comprehensive Foundation. New York: Macmillan, 1994.
  • [10] M. A. Mayosky, G. I. E. Cancelo, “Direct adaptive control of wind energy conversion systems using Gaussian networks”, IEEE Trans. Neural Networks, vol. 10, no. 4, July 1999.
  • [11] M. Sedighizadeh, M. Bayat, A. Rezazadeh, “Nonlinear model identification and adaptive control of variable speed wind turbine using recurrent neural network,” unpublished.
Yıl 2010, Cilt: 2 Sayı: 3, 40 - 46, 01.09.2010

Öz

Kaynakça

  • [1] F. D. Bianchi, H. De Battista and R. J. Mantz, Wind Turbine Control Systems Principles, Modelling and Gain Scheduling Design .Springer-Verlag London Limited 2007.
  • [2] M. N. Eskander, “Neural network Controller for a permanent magnet generator applied n a wind energy conversion systems,” 2002.
  • [3] M.Sedighizadeh and A.Rezazadeh, “Adaptive PID control of wind energy conversion systems using RASPI mother wavelet basis function Networks,” Proceeding of World Academy of Science, Engineering and Technology, vol. 27, February 2008.
  • [4] M.Sedighizadeh and A.Rezazadeh, “Adaptive PID controller based on reinforcement learning for wind turbine control,” Proceeding of World Academy of Science, Engineering and Technology, vol. 27, February 2008.
  • [5] M. Bayat, H. K. Karegar, “Application of Predictive Control in DFIG Wind Turbines” unpublished.
  • [6] P. Simoes, B. K. Bose, and R. J. Spiegel, “Fuzzy logic-based intelligent control of a variable speed cage machine wind generation system,” IEEE Trans. Power Electron., vol. 12, no. 1, Jan. 1997.
  • [7] Z. Chen and S. A. Gomez and M. McCormick, “A Fuzzy logic controlled power electronic systems for variable speed wind energy conversion systems,”.
  • [8] K. Narendra and K. Parthasarathy, “Identification and control of dynamical systems using neural networks,” IEEE Trans. Neural Networks, vol. 1, Mar. 1990.
  • [9] S. Haykin, Neural Networks, a Comprehensive Foundation. New York: Macmillan, 1994.
  • [10] M. A. Mayosky, G. I. E. Cancelo, “Direct adaptive control of wind energy conversion systems using Gaussian networks”, IEEE Trans. Neural Networks, vol. 10, no. 4, July 1999.
  • [11] M. Sedighizadeh, M. Bayat, A. Rezazadeh, “Nonlinear model identification and adaptive control of variable speed wind turbine using recurrent neural network,” unpublished.
Toplam 11 adet kaynakça vardır.

Ayrıntılar

Diğer ID JA65JE26ZE
Bölüm Makaleler
Yazarlar

M. Bayat Bu kişi benim

M. Sedighizadeh Bu kişi benim

A. Rezazadeh Bu kişi benim

Yayımlanma Tarihi 1 Eylül 2010
Yayımlandığı Sayı Yıl 2010 Cilt: 2 Sayı: 3

Kaynak Göster

APA Bayat, M., Sedighizadeh, M., & Rezazadeh, A. (2010). Wind Energy Conversion Systems Control Using Inverse Neural Model Algorithm. International Journal of Engineering and Applied Sciences, 2(3), 40-46.
AMA Bayat M, Sedighizadeh M, Rezazadeh A. Wind Energy Conversion Systems Control Using Inverse Neural Model Algorithm. IJEAS. Eylül 2010;2(3):40-46.
Chicago Bayat, M., M. Sedighizadeh, ve A. Rezazadeh. “Wind Energy Conversion Systems Control Using Inverse Neural Model Algorithm”. International Journal of Engineering and Applied Sciences 2, sy. 3 (Eylül 2010): 40-46.
EndNote Bayat M, Sedighizadeh M, Rezazadeh A (01 Eylül 2010) Wind Energy Conversion Systems Control Using Inverse Neural Model Algorithm. International Journal of Engineering and Applied Sciences 2 3 40–46.
IEEE M. Bayat, M. Sedighizadeh, ve A. Rezazadeh, “Wind Energy Conversion Systems Control Using Inverse Neural Model Algorithm”, IJEAS, c. 2, sy. 3, ss. 40–46, 2010.
ISNAD Bayat, M. vd. “Wind Energy Conversion Systems Control Using Inverse Neural Model Algorithm”. International Journal of Engineering and Applied Sciences 2/3 (Eylül 2010), 40-46.
JAMA Bayat M, Sedighizadeh M, Rezazadeh A. Wind Energy Conversion Systems Control Using Inverse Neural Model Algorithm. IJEAS. 2010;2:40–46.
MLA Bayat, M. vd. “Wind Energy Conversion Systems Control Using Inverse Neural Model Algorithm”. International Journal of Engineering and Applied Sciences, c. 2, sy. 3, 2010, ss. 40-46.
Vancouver Bayat M, Sedighizadeh M, Rezazadeh A. Wind Energy Conversion Systems Control Using Inverse Neural Model Algorithm. IJEAS. 2010;2(3):40-6.

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