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

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Abstract (2. Language): 
This paper introduces an adaptive neuro-fuzzy inference system (ANFIS) model for predicting the power factor of a wind turbine. This model based on the parameters involved for NACA 4415 and LS- 1 profile types with 3 and 4 blades. In model development, profile type, blade number, Schmitz coefficient, end loss, profile type loss, and blade number loss were taken as input variables, while the power factor was taken as output variable. After a successful learning and training process the proposed model produced reasonable mean errors. The results on a testing data indicate that the ANFIS model is found to be more successful than the ANN approach in estimating the power factor.

REFERENCES

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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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