Advertisement

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)

Abstract

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.

Keywords

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

References

  1. 1.
    Fathabadi, H.: Novel filter based ANN approach for short-circuit faults detection, classification and location in power transmission lines. Int. J. Electr. Power Energy Syst. 74, 374–383 (2016)CrossRefGoogle Scholar
  2. 2.
    Hosseini, K.: Short circuit fault classification and location in transmission lines using a combination of wavelet transform and support vector machines. Int. J. Electr. Eng. Inf. 7, 353–365 (2015)Google Scholar
  3. 3.
    Singh, M., Panigrahi, B., Maheshwari, R.P.: Transmission line fault detection, classification. In: International Conference Emerging Trends in Electrical and Computer Technology (ICETECT), Tamil Nadu, pp. 15–22 (2011)Google Scholar
  4. 4.
    Silva, K.M., et al.: Fault detection and classification in transmission lines based on wavelet transform and ANN. IEEE Trans. Power Delivery 21(4), 2058–2063 (2006)CrossRefGoogle Scholar
  5. 5.
    Morais, J., Pires, Y., Cardoso Jr., C., Klautau, A.: A framework for evaluating automatic classification of underlying causes of disturbances and its application to short-circuit faults. IEEE Trans. Power Delivery 25(4), 2083–2094 (2010)CrossRefGoogle Scholar
  6. 6.
    Gowrishankar, M., Nagaveni, P., Balakrishnan, P.: Transmission line fault detection and classification using discrete wavelet transform and artificial neural network. Middle-East J. Sci. Res. 24(4), 1112–1121 (2016)Google Scholar
  7. 7.
    Livani, H., Evrenosoglu, Y.: A machine learning and wavelet-based fault location method for hybrid transmission lines. IEEE Trans. Smart Grid 5(1), 51–59 (2013)CrossRefGoogle Scholar
  8. 8.
    Nayeripour, M., et al.: Fault detection and classification in transmission lines based on a combination of wavelet singular values and fuzzy logic. Cumhuriyet Sci. J. 36(3), 69–82 (2015)Google Scholar
  9. 9.
    Cardoso, C., et al.: Hierarchical Agglomerative clustering of short-circuit faults in transmission lines. In: 10th Brazilian Symposium on Neural Networks, Salvador, Brazil, pp. 87–92 (2008)Google Scholar
  10. 10.
    Das, D., Singh, N.K., Sinha, A.K.: A comparison of Fourier transform, wavelet transform methods for detection, classification of faults on transmission lines. In: IEEE Power India Conference (2006)Google Scholar
  11. 11.
    Zhang, N., Kezunovic, M.: A real time fault analysis tool for monitoring operation of transmission line protective relay. Electr. Power Syst. Res. 77(3–4), 361–370 (2007)CrossRefGoogle Scholar
  12. 12.
    Han, J., Kamber, M., Pei, J.: Data Mining: Concepts and Techniques. Morgan Kaufmann, Burlington (2012)CrossRefzbMATHGoogle Scholar
  13. 13.
    Li, M., Sleep, R.: A robust approach to sequence classification. In: International Conference on Tools with Artificial Intelligence (2005)Google Scholar
  14. 14.
    Telford, R., Galloway, S.: Fault classification and diagnostic system for unmanned aerial vehicle electrical networks based on hidden Markov models. IET Electr. Syst. Transp. 5(3), 103–111 (2015)CrossRefGoogle Scholar
  15. 15.
    Petitjean, F., et al.: Dynamic Time Warping averaging of time series allows faster, more accurate classification. In: 2014 IEEE International Conference on Data Mining, Shenzhen (2014)Google Scholar
  16. 16.
    Priddy, K.L., Keller, P.E.: The curse of dimensionality. In: Artificial Neural Networks: an Introduction, pp. 26–30. SPIE - The International Society for Optical Engineering, Washington (2005)Google Scholar
  17. 17.
    Chandrashekar, G., Sahin, F.: A survey on feature selection methods. Comput. Electr. Eng. 40(1), 16–28 (2014)CrossRefGoogle Scholar
  18. 18.
  19. 19.
    Oshiro, T.M., Perez, P.S., Baranauskas, J.A.: How many trees in a random forest? In: Perner, P. (ed.) MLDM 2012. LNCS (LNAI), vol. 7376, pp. 154–168. Springer, Heidelberg (2012). doi: 10.1007/978-3-642-31537-4_13 CrossRefGoogle Scholar
  20. 20.
    Burges, C.J.C.: A tutorial on support vector machines for pattern recognition. Data Min. Knowl. Disc. 2(2), 121–167 (1998)CrossRefGoogle Scholar
  21. 21.
    Reddy, M.J.B., Mohanta, D.K.: Detection, classification, localization of power system impulsive transients using S-transform. In: 9th International Conference on Environment and Electrical Engineering (EEEIC), Prague, Czech Republic (2010)Google Scholar

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

Personalised recommendations