ENHANCING WIND POWER GENERATION EFFICIENCY: A NOVEL CONTROL ALGORITHM FOR DFIG WIND TURBINES USING FUZZY LOGIC AND NONLINEAR ESTIMATION TECHNIQUES
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Abstract
The control algorithm for a wind turbine equipped with a doubly fed induction generator (DFIG) is presented in detail in this study. A Gaussian radial basis function network—a nonlinear input-output mapping technique—is used to assess the wind turbine's aerodynamic parameters. A nonlinear mapping is used to predict the wind speed. It takes into account the electrical output power of the generator, its power losses, and the dynamics of the wind turbine generator (WTG) shaft system. The creation and thorough assessment of the fuzzy logic controller are part of the new control approach. In the end, the wind generation system uses the technique that this study describes. The calculated wind speed is then utilized to determine the optimal command for the DFIG rotor speed in order to maximize wind power extraction. The Doubly Fed Induction Generator's (DFIG) speed controller is suitably designed to effectively reduce the impact of low-frequency torsional oscillations. The WTG system does away with the requirement for mechanical anemometers while producing the maximum
amount of electrical power output to the grid with exceptional dependability and efficiency.
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References
. H. Karimi-Davijani,1A. Sheikholeslami, H. Livani and M. KarimiDavijani,” Fuzzy Logic
Control of Doubly Fed Induction Generator Wind Turbine”, World Applied Sciences Journal 6
(4): 499-508, 2009, IDOSI Publications, 2009
. Simoes, M. G., Bose, B. K., Spiegel, R. J., Design and Preformance Evaluation of a Fuzzy
Logic- based Variable Speed Wind Generation System, IEEE Transactions on Industry
Applications, Vol. 33, pp. 956- 965, July/August. 1997
. Xibo Yuan, Fei (Fred) Wang, Dushan Boroyevich, Fellow, Yongdong Li and Rolando Burgos,
Member, IEEE “DC-link Voltage Control of a Full Power Converter for Wind Generator
Operating in Weak-Grid Systems” IEEE Transactions On Power Electronics, September 2009.
. Wei Qiao, Wei Zhou, José M. Aller, and Ronald G. Harley, Fellow, IEEE “Wind Speed
Estimation Based Sensorless Output Maximization Control for a Wind Turbine Driving a
DFIG” IEEE transactions on power electronics, may 2008.
. Yuanye Xia, Khaled H. Ahmed, and Barry W. Williams “A New Maximum Power Point
Tracking Technique for Permanent Magnet Synchronous Generator Based Wind Energy
Conversion System” IEEE transactions on power Electronics, vol. 26, no. 12, december 2011.
. D.Aouzellag , K.Ghedamsi,, E.M.Berkouk “ Power Control of a Variable Speed Wind Turbine
Driving an DFIG” Electrical engineering Department, A.Mira University, Bejaïa, Algeria.
. Y¨uksel O˘GUZ1, ˙Irfan GUNEY2 “Adaptive neuro-fuzzy inference system to improve the
power quality of variable-speed wind power generation system” Turk J Elec Eng & Comp
Sci, Vol.18, No.4, 2010, _c TU¨BI˙TAK.
. Evgenije Adzic*, Zoran Ivanovic*, Milan Adzic**, Vladimir Katic “Maximum Power Search
in Wind Turbine Based on Fuzzy Logic Control” Acta Polytechnica Hungarica Vol. 6, No. 1,
. Marcelo Godoy Sim˜oes, Member, IEEE, Bimal K. Bose, Life Fellow, IEEE, and Ronald J.
Spiegel, Member, IEEE, “Design and Performance Evaluation of a Fuzzy-Logic-Based
Variable- Speed Wind Generation System” , IEEE Transactions On Industry Applications,
Vol. 33, No. 4, July/August 1997. [10]. Simoes, M. G., Bose, B. K., Spiegel, R. J., Fuzzy
Logic-based Intelligent trol of a Variable Speed Cage Machine Wind Generation System,
IEEE Transactions on Power Electronics, Vol. 12, pp. 87-95, Jan. 1997.