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
[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[5] M. Bayat, H. K. Karegar, “Application of Predictive Control in DFIG Wind Turbines” unpublished.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“Nonlinear model identification and adaptive control of variable speed wind turbine using recurrent neural network,” unpublished.
Year 2010,
Volume: 2 Issue: 3, 40 - 46, 01.09.2010
[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.
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. September 2010;2(3):40-46.
Chicago
Bayat, M., M. Sedighizadeh, and A. Rezazadeh. “Wind Energy Conversion Systems Control Using Inverse Neural Model Algorithm”. International Journal of Engineering and Applied Sciences 2, no. 3 (September 2010): 40-46.
EndNote
Bayat M, Sedighizadeh M, Rezazadeh A (September 1, 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, and A. Rezazadeh, “Wind Energy Conversion Systems Control Using Inverse Neural Model Algorithm”, IJEAS, vol. 2, no. 3, pp. 40–46, 2010.
ISNAD
Bayat, M. et al. “Wind Energy Conversion Systems Control Using Inverse Neural Model Algorithm”. International Journal of Engineering and Applied Sciences 2/3 (September 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. et al. “Wind Energy Conversion Systems Control Using Inverse Neural Model Algorithm”. International Journal of Engineering and Applied Sciences, vol. 2, no. 3, 2010, pp. 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.