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Abstract

Power system fault identification and classification continues to be one of the most important challenges faced by the power system operators. In spite of the dramatic improvements in this field, the existing protection devices are not able to successfully identify and classify all types of faults which occur power system. The situation in even more the complex in the case of microgrids due to their dynamic behavior and inherent peculiarities. This paper presents a novel method for identification and classification of faults in the microgrid. The proposed method is based on Descrete Wavelet Transform (DWT) and Artificial Neural Networks (ANN). The model is completely developed in MATLAB Simulink and is significant because it can be applied for practical identification and classification of faults in microgrids. The obtained results indicate that the proposed algorithm can be used as a promising foundation for the future implementation of the microgrid protection devices.

References

  1. 1.
    Hosseini, S.A., Abyaneh, H.A., Sadeghi, S.H.H., Razavi, F., Nasiri, A.: An overview of microgrid protection methods and the factors involved. Renew. Sustain. Energy Rev. 64, 174–186 (2016)CrossRefGoogle Scholar
  2. 2.
    Brearley, B.J., Prabu, R.R.: A review on issues and approaches for microgrid protection. Renew. Sustain. Energy Rev. 67, 988–997 (2017)CrossRefGoogle Scholar
  3. 3.
    Laaksonen, H., Hovila, P.: Enhanced MV microgrid protection scheme for detecting high-impedance faults. In: 2017 IEEE Manchester PowerTech, Manchester (2017)Google Scholar
  4. 4.
    Ustun, T.S., Ozansoy, C., Zayegh, A.: Recent developments in microgrids and example cases around the world—A review. Renew. Sustain. Energy Rev. 15, 4030–4041 (2011)CrossRefGoogle Scholar
  5. 5.
    Mohamed, N.A., Salama, M.M.A.: A review on the proposed solutions to microgrid protection problems. In: 2016 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE), Vancouver (2016)Google Scholar
  6. 6.
    Memon, A.A., Kauhaniemi, K.: A critical review of AC Microgrid protection issues and available solutions. Electr. Power Syst. Res. 129, 23–31 (2015)CrossRefGoogle Scholar
  7. 7.
    Lin, H., Guerrero, J.M., Jia, C., Tan, Z.-h., Vasquez, J.C., Liu, C.: Adaptive overcurrent protection for microgrids in extensive distribution systems. In: IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society, Florence (2016)Google Scholar
  8. 8.
    Yu, J.J.Q., Hou, Y., Lam, A.Y.S., Li, V.O.K.: Intelligent fault detection scheme for microgrids with wavelet-based deep neural networks. IEEE Trans. Smart Grid 10(2), 1694–1703 (2019)CrossRefGoogle Scholar
  9. 9.
    Zarrabian, S., Belkacemi, R., Babalola, A.A.: Intelligent mitigation of blackout in real-time microgrids: neural network approach. In: 2016 IEEE Power and Energy Conference at Illinois (PECI), Urbana (2016)Google Scholar
  10. 10.
    Hubana, T.: Coordination of the low voltage microgrid protection considering investment cost. In: 1. Conference BH BH K/O CIRED, Mostar (2018)Google Scholar
  11. 11.
    Hubana, T.: Transmission lines fault location estimation based on artificial neural networks and power quality monitoring data. In: 2018 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe), Sarajevo (2018)Google Scholar
  12. 12.
    Hubana, T., Saric, M., Avdakovic, S.: Approach for identification and classification of HIFs in medium voltage distribution networks. IET Gener. Transm. Distrib. J. 12(5), 1145–1152 (2018)CrossRefGoogle Scholar
  13. 13.
    Hubana, T., Saric, M., Avdakovic, S.: Classification of distribution network faults using Hilbert-Huang transform and artificial neural network. In: IAT 2018: Advanced Technologies, Systems, and Applications III, pp. 114–131. Cham, Springer (2019)Google Scholar
  14. 14.
    Choudhury, M., Ganguly, A.: Transmission line fault classification using discrete wavelet transform. In: International Conference on Energy, Power and Environment: Towards Sustainable Growth (ICEPE), Shillong (2015)Google Scholar
  15. 15.
    Jamil, M., Sharma, S.K., Singh, R.: Fault detection and classification in electrical power transmission system using artificial neural network. SpringerPlus 4, 334 (2015)CrossRefGoogle Scholar
  16. 16.
    Hirsch, A., Parag, Y., Guerrero, J.: Microgrids: A review of technologies, key drivers, and outstanding issues. Renew. Sustain. Energy Rev. 90, 402–411 (2018)CrossRefGoogle Scholar
  17. 17.
    Chaudhary, N.K., Mohanty, S.R., Singh, R.K.: A review on microgrid protection. In: Proceedings of the International Electrical Engineering Congress 2014, Pattaya City, Thailand (2014)Google Scholar
  18. 18.
    Hubana, T., Saric, M., Avdakovic, S.: High-impedance fault identification and classification using a discrete wavelet transform and artificial neural networks. Elektrotehniški Vestn. 85(3), 109–114 (2018)Google Scholar
  19. 19.
    Sarawale, R.K., Chougule, S.R.: Noise removal using double-density dual-tree complex DWT. In: 2013 IEEE Second International Conference on Image Information Processing (ICIIP-2013), Shimla (2013)Google Scholar
  20. 20.
    Haider Mohamad, A.R., Diduch, C.P., Biletskiy, Y., Shao, R., Chang, L.: Removal of measurement noise spikes in grid-connected power converters. In: 2013 4th IEEE International Symposium on Power Electronics for Distributed Generation Systems (PEDG), Shimla (2013)Google Scholar
  21. 21.
    Talebi, S.P., Mandic, D.P.: Frequency estimation in three-phase power systems with harmonic contamination: A multistage quaternion Kalman filtering approach. Imperial College London (2016)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Technical University GrazGrazAustria
  2. 2.Public Enterprise Elektroprivreda of Bosnia and HerzegovinaMostarBosnia and Herzegovina
  3. 3.Faculty of Electrical EngineeringUniversity of SarajevoSarajevoBosnia and Herzegovina

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