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A Voltage Sag Pattern Classification Technique

  • Délio E. B. Fernandes
  • Mário Fabiano Alves
  • Pyramo Pires da CostaJr.
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3776)

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.

Keywords

Power Quality Hierarchical Average Power Quality Disturbance Principal Component Subspace Pattern Classification Technique 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Délio E. B. Fernandes
    • 1
  • Mário Fabiano Alves
    • 1
  • Pyramo Pires da CostaJr.
    • 1
  1. 1.Pontifical Catholic University of Minas Gerais, PUC-MGBelo HorizonteBrazil

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