Doubly fed Induction Generators (DFIGs) are quite common in wind energy
conversion systems because of their variable speed nature and the lower
rating of converters. Magnetic flux saturation in the DFIG significantly affect
its behavior during transient conditions such as voltage sag, sudden change in
input power and short circuit. The effect of including saturation in the DFIG
modeling is significant in determining the transient performance of the
generator after a disturbance. To include magnetic saturation in DFIG model,
an accurate representation of the magnetization characteristics is inevitable.
This paper presents a qualitative modeling for magnetization characteristics
of doubly fed induction generator using neuro-fuzzy systems. Neuro-fuzzy
systems with one hidden layer of Gaussian nodes are capable of
approximating continuous functions with arbitrary precision. The results
obtained are compared with magnetization characteristics obtained using
discrete fourier transform, polynomial and exponential curve fitting. The
error analysis is also done to show the effectiveness of the neuro fuzzy
modeling of magnetizing characteristics. By neuro-fuzzy algorithm, fast
learning convergence is observed and great performance in accuracy is
achieved. 

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