AN ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM APPROACH FOR PREDICTION OF POWER FACTOR IN WIND TURBINES

Abstract

   

Keywords

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References

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):665685.
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 NeuroFuzzy Inference Systems for Analysis of Internal Carotid Arterial Doppler”, Signals Computers in Biology and Medicine, 2005, 35, pp. 687-702.