TY - JOUR
T1 - Novelty detection and multi-class classification in power distribution voltage waveforms
AU - Lazzaretti, André Eugênio
AU - Tax, David Martinus Johannes
AU - Vieira Neto, Hugo
AU - Ferreira, Vitor Hugo
PY - 2016
Y1 - 2016
N2 - The automatic analysis of electrical waveforms is a recurring subject in the power system sector worldwide. In this sense, the idea of this paper is to present an original approach for automatic classification of voltage waveforms in electrical distribution networks. It includes both the classification of the waveforms in multiple known classes, and the detection of new waveforms (novelties) that are not available during the training stage. The classification method, based on the Support Vector Data Description (SVDD), has a suitable formulation for this task, because it is capable of fitting a model on a relatively small set of examples, which may also include negative examples (patterns from other known classes or even novelties), with maximal margin separation. The results obtained on both simulated and real world data demonstrate the ability of the method to identify novelties and to classify known examples correctly. The method finds application in the mitigation process of emergencies normally performed by power utilities' maintenance and protection engineers, which requires fast and accurate event cause identification.
AB - The automatic analysis of electrical waveforms is a recurring subject in the power system sector worldwide. In this sense, the idea of this paper is to present an original approach for automatic classification of voltage waveforms in electrical distribution networks. It includes both the classification of the waveforms in multiple known classes, and the detection of new waveforms (novelties) that are not available during the training stage. The classification method, based on the Support Vector Data Description (SVDD), has a suitable formulation for this task, because it is capable of fitting a model on a relatively small set of examples, which may also include negative examples (patterns from other known classes or even novelties), with maximal margin separation. The results obtained on both simulated and real world data demonstrate the ability of the method to identify novelties and to classify known examples correctly. The method finds application in the mitigation process of emergencies normally performed by power utilities' maintenance and protection engineers, which requires fast and accurate event cause identification.
KW - New class identification
KW - Novelty detection
KW - Open set recognition
KW - Smart grids
KW - Waveform classification
UR - http://www.scopus.com/inward/record.url?scp=84946012667&partnerID=8YFLogxK
U2 - 10.1016/j.eswa.2015.09.048
DO - 10.1016/j.eswa.2015.09.048
M3 - Article
AN - SCOPUS:84946012667
SN - 0957-4174
VL - 45
SP - 322
EP - 330
JO - Expert Systems with Applications
JF - Expert Systems with Applications
ER -