Fault classification for power distribution systems via a combined wavelet-neural approach

This paper presents an integrated design of a fault classifier which uses a hybrid wavelet-artificial neural network (ANN) based approach. The data for the fault classifier is produced by PSCAD/EMTDC simulation program for 34.5 kV Sagmalcilar-Maltepe distribution system in Istanbul, Turkey. It is aimed to design a classifier capable of recognizing ten classes of three-phase distribution system faults. A database of line currents and line-to-ground voltages is built up including system faults at different fault inception angles and fault locations. The characteristic information over six-channel of current and voltage samples is extracted by the wavelet multiresolution analysis technique. Then, an ANN-based tool was employed for classification task. The main idea of this approach is to solve the complex fault (three-phase short-circuit) classification problem under various system and fault conditions. A self-organizing map, with Kohonen´s learning algorithm and type-one learning vector quantization technique is implemented into this study. The performance of the wavelet-neural fault classifier is presented and the results are analyzed in the paper. It is shown that the technique correctly recognizes and discriminates the fault types and faulted phases with a high degree of accuracy in the simulated model distribution system.