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

Year 2009, Volume: 9 Issue: 1, 905 - 912, 14.02.2012

Abstract

   

References

  • Global Wind Energy Council News.
  • http://www.wwindea.org/home/images/storie s/pr statistics 2007_210208_red.pdf, World WindEnergy Association press release retrieved 2008 03 18.
  • 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.
  • 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.
  • 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.
  • A. Sfetsos, “A comparison of various forecasting techniques applied to mean hourly wind speed time series”, Renewable Energy, 21, 2000, pp. 23-35.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • J.S.R. Jang, “ANFIS: Adaptive-Networkbased Fuzzy Inference System”, IEEE Trans Systems, Man and Cybernetics, 1993; 23(3):665685.
  • J.S.R. Jang, C.T. Sun, “Neuro-Fuzzy Modeling and Control”, Proceedings of the IEEE, 1995, Vol:83, No.3.
  • B. Kosko, “Neural Networks and Fuzzy Systems, A Dynamical Systems Approach”, Englewood Ciffs., 1991, NJ: Prentice Hall.
  • 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.
  • 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.
  • 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.
Year 2009, Volume: 9 Issue: 1, 905 - 912, 14.02.2012

Abstract

References

  • Global Wind Energy Council News.
  • http://www.wwindea.org/home/images/storie s/pr statistics 2007_210208_red.pdf, World WindEnergy Association press release retrieved 2008 03 18.
  • 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.
  • 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.
  • 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.
  • A. Sfetsos, “A comparison of various forecasting techniques applied to mean hourly wind speed time series”, Renewable Energy, 21, 2000, pp. 23-35.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • J.S.R. Jang, “ANFIS: Adaptive-Networkbased Fuzzy Inference System”, IEEE Trans Systems, Man and Cybernetics, 1993; 23(3):665685.
  • J.S.R. Jang, C.T. Sun, “Neuro-Fuzzy Modeling and Control”, Proceedings of the IEEE, 1995, Vol:83, No.3.
  • B. Kosko, “Neural Networks and Fuzzy Systems, A Dynamical Systems Approach”, Englewood Ciffs., 1991, NJ: Prentice Hall.
  • 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.
  • 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.
  • 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.
There are 18 citations in total.

Details

Primary Language English
Journal Section Articles
Authors

Rasit Ata This is me

Publication Date February 14, 2012
Published in Issue Year 2009 Volume: 9 Issue: 1

Cite

APA Ata, R. (2012). AN ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM APPROACH FOR PREDICTION OF POWER FACTOR IN WIND TURBINES. IU-Journal of Electrical & Electronics Engineering, 9(1), 905-912.
AMA Ata R. AN ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM APPROACH FOR PREDICTION OF POWER FACTOR IN WIND TURBINES. IU-Journal of Electrical & Electronics Engineering. February 2012;9(1):905-912.
Chicago Ata, Rasit. “AN ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM APPROACH FOR PREDICTION OF POWER FACTOR IN WIND TURBINES”. IU-Journal of Electrical & Electronics Engineering 9, no. 1 (February 2012): 905-12.
EndNote Ata R (February 1, 2012) AN ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM APPROACH FOR PREDICTION OF POWER FACTOR IN WIND TURBINES. IU-Journal of Electrical & Electronics Engineering 9 1 905–912.
IEEE R. Ata, “AN ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM APPROACH FOR PREDICTION OF POWER FACTOR IN WIND TURBINES”, IU-Journal of Electrical & Electronics Engineering, vol. 9, no. 1, pp. 905–912, 2012.
ISNAD Ata, Rasit. “AN ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM APPROACH FOR PREDICTION OF POWER FACTOR IN WIND TURBINES”. IU-Journal of Electrical & Electronics Engineering 9/1 (February 2012), 905-912.
JAMA Ata R. AN ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM APPROACH FOR PREDICTION OF POWER FACTOR IN WIND TURBINES. IU-Journal of Electrical & Electronics Engineering. 2012;9:905–912.
MLA Ata, Rasit. “AN ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM APPROACH FOR PREDICTION OF POWER FACTOR IN WIND TURBINES”. IU-Journal of Electrical & Electronics Engineering, vol. 9, no. 1, 2012, pp. 905-12.
Vancouver Ata R. AN ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM APPROACH FOR PREDICTION OF POWER FACTOR IN WIND TURBINES. IU-Journal of Electrical & Electronics Engineering. 2012;9(1):905-12.