On-line supervised learning for dynamic security classification using probabilistic neural networks

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dc.contributor.author Gavoyiannis, AE en
dc.contributor.author Voumvoulakis, EM en
dc.contributor.author Hatziargyriou, ND en
dc.date.accessioned 2014-03-01T02:43:27Z
dc.date.available 2014-03-01T02:43:27Z
dc.date.issued 2005 en
dc.identifier.uri http://hdl.handle.net/123456789/31418
dc.subject Dynamic Security en
dc.subject Gaussian Mixtures en
dc.subject Isolated Systems en
dc.subject Maximum Likelihood (ML) en
dc.subject On-line learning en
dc.subject Probabilistic Neural Network (PNN) en
dc.subject Probability Density Function (PDF) en
dc.subject.other Dynamic security en
dc.subject.other Gaussian mixtures en
dc.subject.other Isolated systems en
dc.subject.other On-line learning en
dc.subject.other Probabilistic neural network (PNN) en
dc.subject.other Classification (of information) en
dc.subject.other Decision making en
dc.subject.other Electric power systems en
dc.subject.other Maximum likelihood estimation en
dc.subject.other Online systems en
dc.subject.other Pattern recognition en
dc.subject.other Probability density function en
dc.subject.other Security systems en
dc.subject.other Neural networks en
dc.title On-line supervised learning for dynamic security classification using probabilistic neural networks en
heal.type conferenceItem en
heal.identifier.primary 10.1109/PES.2005.1489656 en
heal.identifier.secondary http://dx.doi.org/10.1109/PES.2005.1489656 en
heal.publicationDate 2005 en
heal.abstract This paper addresses the problem of dynamic security classification of electric power systems using multiclass pattern recognition. In particular, on-line supervised learning using Probabilistic Neural Networks is applied. The various patterns are recognized by calculating probabilities of belonging to each class. These probabilities are used in a subsequent decision-making stage to achieve classification. The learning of each class can be performed in parallel. Results regarding performance of the proposed pattern recognition tested on the dynamic security of an actual island power system are presented and discussed. © 2005 IEEE. en
heal.journalName 2005 IEEE Power Engineering Society General Meeting en
dc.identifier.doi 10.1109/PES.2005.1489656 en
dc.identifier.volume 3 en
dc.identifier.spage 2669 en
dc.identifier.epage 2675 en

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