A classifier for distribution feeder overcurrent analysis

This paper focuses on the problem of differentiating inrush currents from fault currents that are observed for a feeder at a distribution substation. This problem is important for feeder protection and power-quality monitoring purposes. The paper shows, using the actual field data, that it is not always easy to distinguish inrush currents from fault currents-as they do not have always well-defined waveforms. This paper shows that the two approaches-the Fourier transform, and the Wavelet transform can be adopted to extract features that make it possible to distinguish them from each other by using an artificial-neural-network-based classifier. The paper also illustrates how to address the issues for successful implementation of these schemes; such as prescreening of data, how to apply fast Fourier transform (FFT) and wavelet transform on the data, and the training of the artificial neural network in order to maximize the performance of the classifier. These issues are illustrated using the actual field data. The test results indicate that both FFT-based and wavelet-transform (WT)-based classifiers yield good results, but the WT-based classifier has better performance.