Improvement in the performance of neural network-based power transmission line fault classifiers

Abstract

A power line expert can easily pinpoint the type of fault that may have been occurred in a power transmission line. Transferring the experts intelligence to an artificial neural network (NN) makes the classification process fast and available online. Often the phase currents are used as NN inputs for this purpose. Lack of a somehow one-to-one relationship between the type of fault and the phases faulty currents prohibits the underlying network from being adequately trained. In a search for finding a type of feature that establishes a relatively unique link between the type of faults and the phase currents, it is noticed and mathematically proved that the ratios of the phase current jumps enjoy such a valuable advantage to be a prime choice as NN inputs. The inputs let a multi-layer perceptron (MLP) NN with about one node per phase to identify the faults accurately. The scheme works well in the presence of a various number of fault items. The superiority of the method is well realised when it is compared with the results of similar investigations using wavelet, fuzzy and others. The reference data are generated using MATLAB Power System Toolbox. The test samples are more general than those previously used in other investigations.