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Data Mining Applied to the Electric Power Industry: Classification of Short-Circuit Faults in Transmission Lines

  • Yomara Pires
  • Jefferson Morais
  • Claudomir Cardoso
  • Aldebaro Klautau
Part of the Studies in Computational Intelligence book series (SCI, volume 169)

Abstract

Data mining can play a fundamental role in modern power systems. However, the companies in this area still face several difficulties to benefit from data mining. A major problem is to extract useful information from the currently available non-labeled digitized time series. This work focuses on automatic classification of faults in transmission lines. These faults are responsible for the majority of the disturbances and cascading blackouts. To circumvent the current lack of labeled data, the Alternative Transients Program (ATP) simulator was used to create a public comprehensive labeled dataset. Results with different preprocessing (e.g., wavelets) and learning algorithms (e.g., decision trees and neural networks) are presented, which indicate that neural networks outperform the other methods.

Keywords

Transmission Line Data Mining Technique Power Quality Frame Length Data Mining Apply 
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 2009

Authors and Affiliations

  • Yomara Pires
    • 1
  • Jefferson Morais
    • 1
  • Claudomir Cardoso
    • 1
  • Aldebaro Klautau
    • 1
  1. 1.Signal Processing Laboratory (LaPS)Federal University of Pará (UFPA)BelémBrazil

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