High Impedance Fault Classification and Contact Surface Determination Employing Pattern Recognition Methods

In this paper, pattern recognition algorithms are employed to detect and classify the type of high impedance faults (broken and unbroken) and in case of broken ones to determine the surface (gravel, asphalt and concrete) which the conductor has become in contact with it in power distribution networks. These methods are multilayer SVM and Fuzzy ART classifiers on the bases of features extracted by S-transform and TTtransform from feeder one cycle post-fault current waveforms. These features include energy, standard deviation and median absolute deviation. The proposed algorithms have been tested on different data set, obtained from field tests and simulated data for events with similar characteristics. The results have shown that the features which are extracted by applying TT-transform contain more information and separability characteristics than those extracted by S-transform and also Fuzzy ART classifier has more accuracy in comparison with multilayer SVM.