Abstract
This paper presents a new approach for automatic oscillography classification in distribution networks, including the detection of patterns not initially presented to the classifier during training, which are defined as novelties. We performed experiments with coupled novelty detection and multi-class classification, and also in separate stages, using the following classifiers: Gaussian Mixture Models (GMM), K-means clustering (KM), K-nearest neighbors (KNN), Parzen Windows (PW), Support Vector Data Description (SVDD), and multi-class classification based on Support Vector Machines (SVM). Preliminary results for simulated data in the Alternative Transient Program (ATP) demonstrate the ability of the method to identify new classes of events in a dynamic learning environment. This work was partially supported by COPEL within the Research and Development Program of the Brazilian Electrical Energy Agency (ANEEL).
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