Autonomous neural models for the classification of events in power distribution networks

Abstract

This paper presents a method for automatic classification of faults and transients in power distribution networks, based on voltage oscillographies of the distribution networks feeders. For signal preprocessing, the discrete wavelet transform was used with the performances of several families of wavelet functions being compared. In the classification stage, three neural models were assessed: multilayer perceptrons, radial basis function networks, and support vector machines. The models were trained autonomously, i.e., using automatic model selection and complexity control. Promising results were obtained using a set of simulations generated using the Alternative Transients Program (ATP). Initial results obtained for real data acquired from a set of oscillograph loggers installed in a distribution network are also presented.