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Classification of Distribution Network Faults Using Hilbert-Huang Transform and Artificial Neural Network

  • Tarik HubanaEmail author
  • Mirza Šarić
  • Samir Avdaković
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 59)

Abstract

Identification and classification of faults in the electrical power system remain one of the most important tasks for the system operators and managers. In particular, high impedance faults (HIF) identification and classification is an especially challenging task due to the physical properties of the waveforms and low neutral voltage. In recent years, there have been significant technology driven advances in this field of research, owned to the introduction and progress of smart grid technologies. However, this topic still remains an open area of research. This paper presents a method for classification of HIF in a medium voltage distribution network, based on the Hilbert-Huang Transform and Artificial Neural Networks. This method was tested on generated signals based on the model of a realistic distribution system. The results indicated that the proposed algorithm is capable to accurately classify HIF in the distribution system. This paper contributes to the existing research by developing and testing, on a model of realistic distribution system, a HIF classification method which offers very efficient and accurate performance.

References

  1. 1.
    Hubana, T., Begić, E., Šarić, M.: Voltage sag propagation caused by faults in medium voltage distribution network. In: Hadžikadić, M., Avdaković, S. (eds.) Advanced Technologies, Systems, and Applications II, IAT 2017. Lecture Notes in Networks and Systems, vol. 28. Springer, Cham (2018)Google Scholar
  2. 2.
    Hanninen, S.: Single phase earth faults in high impedance grounded networks. Technical Research Centre of Finland, VTT Publications, Espoo (2001)Google Scholar
  3. 3.
    Wang, B., Geng, J., Dong, X.: High-impedance fault detection based on non-linear voltage-current characteristic profile identification. IEEE Trans. Smart Grid 9(4), 3783–3791 (2017)CrossRefGoogle Scholar
  4. 4.
    Hubana, T., Šarić, M., Avdaković, S.: Approach for identification and classification of HIFs in medium voltage distribution networks. IET Gener. Transm. Distrib. 12(5), 1145–1152 (2018)CrossRefGoogle Scholar
  5. 5.
    Šarić, M., Hubana, T., Begić, E.: Fuzzy logic based approach for faults identification and classification in medium voltage isolated distribution network. In: Hadžikadić, M., Avdaković, S. (eds.) Advanced Technologies, Systems, and Applications II, IAT 2017. Lecture Notes in Networks and Systems, vol 28. Springer, Cham (2018)Google Scholar
  6. 6.
    Jamil, M., Sharma, S., Singh, R.: Fault detection and classification in electrical power transmission system using artificial neural network. Springer Plus 4, 334 (2015).  https://doi.org/10.1186/s40064-015-1080-xCrossRefGoogle Scholar
  7. 7.
    Guo, Y., Li, C., Li, Y., Gao, S.: Research on the power system fault classification based on HHT and SVM using wide-area information. Energy Power Eng. 5(4B), 138–142 (2013).  https://doi.org/10.4236/epe.2013.54B026CrossRefGoogle Scholar
  8. 8.
    Bradley, B.L.: The Hilbert-Huang Transform: theory, applications, development - PhD thesis. University of Iowa, Iowa City (2011)Google Scholar
  9. 9.
    Huang, N.E., Wu, Z.: A review on Hilbert-Huang transform: method and its applications to geophysical studies. Rev. Geophys. 46(2), 1944:9208 (2008)Google Scholar
  10. 10.
    Ayyagari, S.B.: Artificial Neural Network Based Fault Location for Transmission Lines - Master’s theses. University of Kentucky, Lexington (2011)Google Scholar
  11. 11.
    Haykin, S.: Neural Networks: A Comprehensive Foundation. Macmillan College Publishing Company, London (1994)zbMATHGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Public Enterprise Elektroprivreda of Bosnia and Herzegovina MostarMostarBosnia and Herzegovina
  2. 2.Public Enterprise Elektroprivreda of Bosnia and Herzegovina SarajevoSarajevoBosnia and Herzegovina

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