A new approach for event classification and novelty detection in power distribution networks
| Autor: | André Eugênio Lazzaretti, Hugo Vieira Neto, Vitor Hugo Ferreira, Luiz Felipe Ribeiro Barrozo Toledo, Cleverson L. S. Pinto |
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| Rok vydání: | 2013 |
| Předmět: |
Distribution networks
Computer science business.industry Pattern recognition Mixture model computer.software_genre Novelty detection Support vector machine symbols.namesake ComputingMethodologies_PATTERNRECOGNITION symbols Artificial intelligence Data mining business Cluster analysis Classifier (UML) Gaussian process computer |
| Zdroj: | 2013 IEEE Power & Energy Society General Meeting. |
| DOI: | 10.1109/pesmg.2013.6672703 |
| Popis: | 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). |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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