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


Fulltext : http://zet10.ipee.pwr.wroc.pl/record/300/files/

Cited by: try citation search for ENY-ARTICLE-2009-239

Similar records

 

People who viewed this page also viewed:

(1111)  Proceedings of the Int. Conference "7th EEEIC International Workshop on Environment and Electrical Engineering" - Schwarz, H et al - ENY-PREPRINT-2008-008 (1082)  Evaluation of compensation devices of DC arc furnaces using advanced spectrum estimation methods - Bracale, A et al - ENY-ARTICLE-2008-011 (1076)  Impact of Distributed Generation on Electrical Power Network - Khan, U N - ENY-ARTICLE-2008-142 (1067)  Adaptacyjne sieci neuronowe w zastosowaniu do identyfikacji stanu układów elektrycznych - Kostyla, P - ENY-THESIS-2008-005 (1057)  Parametric methods for time-frequency analysis of electric signals - Leonowicz, Z - ENY-BOOK-2008-001

 

Rate this document:

Be the first to review this document.