Training an artificial neural network to discriminate between magnetizing inrush and internal faults

A feedforward neural network (FFNN) has been trained to discriminate between power transformer magnetizing inrush and fault currents. The training algorithm used was backpropagation, assuming initially a sigmoid transfer function for the network's processing units ("neurons"). Once the network was trained the units' transfer function was changed to hard limiters with thresholds equal to the biases obtained for the sigmoids during training. The off-line experimental results presented in this paper show that a FFNN may be considered as an alternative method to make the discrimination between inrush and fault currents in a digital relay implementation.<
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