A voltage sag pattern classification technique

  • Authors:

  • Délio E. B. Fernandes;Mário Fabiano Alves;Pyramo Pires da Costa, Jr

  • Affiliations:

  • Pontifical Catholic University of Minas Gerais, PUC-MG, Belo Horizonte, MG, Brazil;Pontifical Catholic University of Minas Gerais, PUC-MG, Belo Horizonte, MG, Brazil;Pontifical Catholic University of Minas Gerais, PUC-MG, Belo Horizonte, MG, Brazil

  • Venue:
  • PReMI'05 Proceedings of the First international conference on Pattern Recognition and Machine Intelligence
  • Year:
  • 2005

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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.