Categorization of power quality transients using higher-order statistics and competitive layers-based neural networks

This paper deals with power-quality (PQ) event detection, classification and characterization using higher-order sliding cumulants (which are calculated over high-pass filtered signals to avoid the low-frequency 50-Hz sinusoid), whose maxima and minima are the coordinates of two-dimensional feature vectors. The classification strategy is based in competitive layers. We focus on the problem of differentiating two types of transients: short-duration (impulsive transients) and long-duration (oscillatory transients). The results show that the measured vectors are classified into clearly differentiated clusters in the feature space. The experience aims to set the foundations of an automatic procedure for PQ event detection.