A New Strategy Based on Feature Selection for Fault Classification in Transmission Lines

  • Márcia HomciEmail author
  • Paulo Chagas
  • Brunelli Miranda
  • Jean Freire
  • Raimundo ViégasJr.
  • Yomara Pires
  • Bianchi Meiguins
  • Jefferson Morais
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10022)


The transmission lines are the element most susceptible to faults on power systems, and the short circuit faults are the worst type of faults than can happen on this element. In order to avoid further problems due to these faults, a fault diagnostic is necessary, and the use of front ends is required. However, the selection process for choosing the front ends is not a simple one because it behaves differently for each. Therefore, this paper presents a new front end, called Concat front end, which integrates other front ends, such as wavelet, raw and Root Mean Square. Furthermore, we have applied feature selection techniques based on filter in order to decrease the dimension of the input data. Thus, we used the following classifiers: neural network, K-nearest neighbor, Random Forest and support vector machine. We used a public dataset called UFPAFaults to train and test the classifiers. As a result, the concatenation of front ends, on most cases, had achieved the lowest error rates. In addition, the feature selection techniques applied showed that it is possible to get higher accuracy using less features on the process.


Short-circuit fault Transmission lines Front ends Feature selection Machine learning algorithms 


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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Márcia Homci
    • 1
    Email author
  • Paulo Chagas
    • 1
  • Brunelli Miranda
    • 1
  • Jean Freire
    • 1
  • Raimundo ViégasJr.
    • 1
  • Yomara Pires
    • 1
  • Bianchi Meiguins
    • 1
  • Jefferson Morais
    • 1
  1. 1.Federal University of PáraBelémBrazil

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