A probabilistic neural network classifier-based method for transformer winding fault identification through its transfer function measurement

In this paper, a new method is introduced for identification of transformer winding fault through transfer function analysis. For this analysis, vector fitting and probabilistic neural network are used. The results of transfer functions estimation through vector fitting are employed for training of neural network, and consequently, probabilistic neural network is used for classification of faults. The required data for fault type identification are obtained by measurements on two groups of transformers (one is a classic 20 kV transformer, and the other is a model transformer) under intact condition and under different fault conditions (axial displacement, radial deformation, disc space variation, and short circuit of winding). Comparing the results of this new method with the well-known published work in the literature, the superior capabilities of this proposed method are demonstrated. Copyright (c) 2011 John Wiley & Sons, Ltd.