New Trends in Power Quality Event Analysis: Novelty Detection and Unsupervised Classification

A new method for automatic event–cause classification in power distribution networks for the detection and clustering of previously unknown classes of transient voltage waveforms is presented. The approach performs the detection of novelties—events that are not present during modeling of the classifier—in addition to the classification of known events, using a formulation based on support vector data description. Additionally, an unsupervised clustering method for novelties is proposed, in order to collect relevant information about their features and allow identification of new classes of events, which constitutes the main contribution of this work. Two different automatic clustering methods are compared: X-Means clustering and Rival Penalized Expectation Maximization. Experiments using both simulated and real data for the entire classification process, which includes multi-class classification with novelty detection and identification of new classes, are presented. The results obtained demonstrate that the proposed method fully agrees with current trends in smart distribution networks, in which automatic identification, characterization, and mitigation of events are critical for network operation and maintenance.