Abstract
This paper presents an investigation on pattern classification techniques applied to voltage sag monitoring data. Similar pattern groups or sets of classes, resulting from a voltage sag classification, represent disturbance categories that may be used as indexes for a cause/effect disturbance analysis. Various classification algorithms are compared in order to establish a classifier design. Results over clustering performance indexes are presented for hierarchical, fuzzy c-means and k-means unsupervised clustering techniques, and a principal component analysis is used for features (or attributes) choice. The efficiency of the algorithms was analyzed by applying the CDI and DBI indexes.
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Keywords
- Power Quality
- Hierarchical Average
- Power Quality Disturbance
- Principal Component Subspace
- Pattern Classification Technique
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References
Dugan, R.C., McGranaghan, M.F., Beaty, H.W.: Eletrical Power Systems Quality. McGraw-Hill, New York (1996)
Alves, M.F., Fernandes, D.E.: Development of an Automated Power Quality Management System. In: 1999 IEEE/PES Transmission and Distribution Conference, New Orleans (1999)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, p. 654. John Willey & Sons, New York (2001)
Morcos, M.M., Ibrahim, W.R.A.: Artificial Intelligence Advanced Mathematical Tools for Power Quality Applications. IEEE Transactions on Power Delivery 17(2) (June 2002)
Chicco, G., Naopli, R., Piglioni, F.: Comparisons among Clustering Techniques for Electricity Customer Classification. In: IEEE Bologna Power Tech., Bologna, Italy (June 2003)
Johnson, R.A., Wichern, D.W.: Applied Multivariate Statistical Analysis, 3rd edn., p. 642. Prentice Hall, New Jersey (1992)
Jain, A.K., Murty, M.N., Flynn, P.J.: Data Clustering: A Review. ACM Computer Survey 31(3), 264–323 (1999)
Haykin, S.: Neural Networks - A Comprehensive Foundation, p. 696. Macmillan Publishing Company, USA (1994)
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© 2005 Springer-Verlag Berlin Heidelberg
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Fernandes, D.E.B., Alves, M.F., da Costa, P.P. (2005). A Voltage Sag Pattern Classification Technique. In: Pal, S.K., Bandyopadhyay, S., Biswas, S. (eds) Pattern Recognition and Machine Intelligence. PReMI 2005. Lecture Notes in Computer Science, vol 3776. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11590316_34
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DOI: https://doi.org/10.1007/11590316_34
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-30506-4
Online ISBN: 978-3-540-32420-1
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