Comparison studies of five neural network based fault classifiers for complex transmission lines

The application of neural networks to power systems has been extensively reported. In the field of protection, neural network based-protection techniques have been proposed by a number of investigators including the authors. However, almost all the studies have so far employed the backpropagation neural network structure with supervised learning. It is the purpose of this paper to report some recent studies on different neural network models, particularly those with combined supervised/unsupervised learning applied to fault classification for complex transmission lines. The neural networks concerned here include: (i) the backpropagation net; (ii) the feature-map net; (iii) the radial basis function net; (iv) the counter-propagation net; and (v) the learning vector quantization net. Special emphasis is placed on a comparison of the performance of the five neural networks in terms of size of the neural network, learning process, classification accuracy and robustness. The outcome of the work serves and provides guidelines on how to select a particular neural network from a number of different neural networks for a specific application