[1] Global Wind Energy Council News.
[2] http://www.wwindea.org/home/images/storie
s/pr statistics 2007_210208_red.pdf, World
WindEnergy Association press release retrieved
2008 03 18.
[3] M.A. Yurdusev, R. Ata and N.S. Çetin,
“Assessment of optimum tip speed ratio in wind
turbines using artificial neural networks”,
Energy, 2006, 31:1817-1825.
[4] R. Ata, N.S. Çetin, “Neural Prediction of
Power Factor in Wind Turbines” Istanbul
University Journal of Electrical & Electronics
Engineering, 2007, Vol:7, No.2, pp. 431-438.
[5] E. Cam and O. Yıldız, “Prediction of wind
speed and power in the central Anatolian region
of Turkey by adaptive neuro-fuzzy inference
systems (ANFIS)”, Turkish J. Eng. Env. Sci. 30
2006, pp. 35- 41.
[6] A. Sfetsos, “A comparison of various forecasting
techniques applied to mean hourly wind
speed time series”, Renewable Energy, 21, 2000,
pp. 23-35.
[7] C. Potter, M. Ringrose and M. Negnevitsky,
“Short-term wind forecasting techniques for
power generation”, Australasian Universities
Power Engineering Conference (AUPEC 2004),
26-29 September, 2004, Brisbane, Australia.
[8] M. Negnevitsky and C.W. Potter, “Innovative
short-term wind generation prediction techniques”,
Power Systems Conference and
Exposition, IEEE PES, Oct. 29, 2006-Nov. 1
2006, pp. 60-65.
[9] M. Negnevitsky, P. Johnson and S. Santoso,
“Short term wind power forecasting using hybrid
intelligent systems”, Power Engineering Society
General Meeting, IEEE 24-28 June 2007, pp.1-4.
[10] M. Alata, M.A. Al-Nimr and Y. Qaroush,
“Developing a multipurpose sun tracking system
using fuzzy control”, Energy Conversion and
Management, Vol:46, Iss. 7-8, May 2005, pp.
1229-1245.
[11] A. Mellit, S.A. Kalogirou, S. Shaari, H.
Salhi and A. Hadj Arab, “Methodology for
predicting sequences of mean monthly clearness
index and daily solar radiation data in remote
areas: Application for sizing a stand-alone PV
system”, Renewable Energy, In Press, Corrected
Proof, Available online 24 October 2007.
[12] M.H. Kazeminezhad, A. Etemad-Shahidi,
and S.J. Mousavi, “Application of fuzzy
inference system in the prediction of wave
parameters”, Ocean Engineering, 32, 2005, pp.
1709-1725.
[13] J.S.R. Jang, “ANFIS: Adaptive-Networkbased
Fuzzy Inference System”, IEEE Trans
Systems, Man and Cybernetics, 1993; 23(3):665-
685.
[14] J.S.R. Jang, C.T. Sun, “Neuro-Fuzzy
Modeling and Control”, Proceedings of the
IEEE, 1995, Vol:83, No.3.
[15] B. Kosko, “Neural Networks and Fuzzy
Systems, A Dynamical Systems Approach”,
Englewood Ciffs., 1991, NJ: Prentice Hall.
[16] C. Potter and M. Negnevitsky, “Short Term
Power System Forecasting Using an Adaptive
Neural-Fuzzy Inference System”, Australian and
New Zealand Intelligent Information Systems
Conference (ANZIIS), 2003, Vol:8, pp. 465-470.
[17] C. Potter, M. Ringose and M. Negnevitsky,
“Short-Term Wind Forecasting Techniques For
Power Generation”, Australasian Universities
Power Engineering Conference (AUPEC 2004),
26-29 September 2004, Brisbane, Australia.
[18] E.D. Ubeyli and I. Guler, “Adaptive Neuro-
Fuzzy Inference Systems for Analysis of Internal
Carotid Arterial Doppler”, Signals Computers in
Biology and Medicine, 2005, 35, pp. 687-702.
Rasit Ata was born in Manisa, Turkey on May 8,
1968. He received B.S., M. S. and Ph. D. degrees in
Electrical Engineering all from the University of
Yıldız in 1991, 1993, 1997, respectively. In 1992 he
An Adaptive Neuro-Fuzzy Inference System Approach For Prediction Of Power Factor In Wind Turbines
Rasit ATA
912
joined the Department of Electrical Engineering at the
same university, then he joined the Department of
Electrical Engineering, Celal Bayar University in 1994
and became an assistant professor there in 2002. He is
engaged in the research of power systems, renewable
energy and artificial neural networks.
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