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Published Article / ENY-ARTICLE-2009-239

Automated Classification of Power Quality Disturbances using SVM and RBF Networks

Janik, P (Wroclaw University of Technology, Poland) ; Lobos, T (Wroclaw University of Technology, Poland)


Abstract: The authors propose a new method of power quality classification using SVM (Support Vector Machine) neural networks. Classifier based on RBF networks (Radial Basis Function) was in parallel applied to enable proper performance comparison. Both, RBF and SVM networks are introduced and considered an appropriate tool for classification problems. Space phasor is used for feature extraction from three-phase signals, to build distinguished patterns for classifiers. In order to create training and testing vectors different disturbance classes were simulated (e.g. sags, voltage fluctuations, transients) in Matlab. Finally, the investigation results of the novel approach are shown and interpreted.

Keyword(s): Power Quality ; Disturbance Classification, ; Neural Networks ; Space Phasor ; Support Vector Machines
Note: 12th International Conference on Harmonics and Quality of Power, Cascais, Portugal, 1-5 Oct 2006, paper No. 3A.1

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Record created 2009-01-30, last modified 2009-01-31

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