Skip to main content

Deep learning for high-impedance fault detection and classification: transformer-CNN

Abstract

High-impedance faults (HIFs) exhibit low current amplitude and highly diverse characteristics, which make them difficult to be detected by conventional overcurrent relays. Various machine learning (ML) techniques have been proposed to detect and classify HIFs; however, these approaches are not reliable in presence of diverse HIF and non-HIF conditions and, moreover, rely on resource-intensive signal processing techniques. Consequently, this paper proposes a novel HIF detection and classification approach based on a state-of-the-art deep learning model, the transformer network, stacked with the Convolutional neural network (CNN). While the transformer network learns the complex HIF pattern in the data, the CNN enhances the generalization to provide robustness against noise. A kurtosis analysis is employed to prevent false detection of non-fault disturbances (e.g., capacitor and load switching) and nonlinear loads as HIFs. The performance of the proposed HIF detection and classification approach is evaluated using the IEEE 13-node test feeder. The results demonstrate that the proposed protection method reliably detects and classifies HIFs, is robust against noise, and outperforms the state-of-the-art techniques.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20

References

  1. Wang B, Geng J, Dong X (2018) High-impedance fault detection based on nonlinear voltage-current characteristic profile identification. IEEE Trans Smart Grid 9(4):3783–3791

    Article  Google Scholar 

  2. Gautam S, Brahma SM (2013) Detection of high impedance fault in power distribution systems using mathematical morphology. IEEE Trans Power Syst 28(2):1226–1234

    Article  Google Scholar 

  3. Wei M, Liu W, Zhang H, Shi F, Chen W (2021) Distortion-based detection of high impedance fault in distribution systems. IEEE Trans Power Deliv 36(3):1603–1618

    Article  Google Scholar 

  4. Yeh H-G, Sim S, Bravo RJ (2019) Wavelet and denoising techniques for real-time HIF detection in 12-kv distribution circuits. IEEE Syst J 13(4):4365–4373

    Article  Google Scholar 

  5. Wang S, Dehghanian P (2020) On the use of artificial intelligence for high impedance fault detection and electrical safety. IEEE Trans Ind Appl 56(6):7208–7216

    Article  Google Scholar 

  6. Ghaderi A, Ginn HL III, Mohammadpour HA (2017) High impedance fault detection: a review. Electr Power Syst Res 143:376–388

    Article  Google Scholar 

  7. Cui Q, Weng Y (2020) Enhance high impedance fault detection and location accuracy via \(\mu \) -PMUs. IEEE Trans Smart Grid 11(1):797–809

    Article  Google Scholar 

  8. Iurinic LU, Herrera-Orozco AR, Ferraz RG, Bretas AS (2016) Distribution systems high-impedance fault location: a parameter estimation approach. IEEE Trans Power Deliv 31(4):1806–1814

    Article  Google Scholar 

  9. Kwon WH, Lee GW, Park YM, Yoon MC, Yoo MH (1991) High impedance fault detection utilizing incremental variance of normalized even order harmonic power. IEEE Trans Power Deliv 6(2):557–564

    Article  Google Scholar 

  10. Sheng Y, Rovnyak SM (2004) Decision tree-based methodology for high impedance fault detection. IEEE Trans Power Deliv 19(2):533–536

    Article  Google Scholar 

  11. Girgis AA, Chang W, Makram EB (1990) Analysis of high-impedance fault generated signals using a Kalman filtering approach. IEEE Trans Power Deliv 5(4):1714–1724

    Article  Google Scholar 

  12. Lima, É.M., dos Santos Junqueira, C.M., Brito, N.S.D., de Souza, B.A., de Almeida Coelho, R., de Medeiros, H.G.M.S.: High impedance fault detection method based on the short-time Fourier transform. IET Gener. Transm. Distrib. 12(11), 2577–2584 (2018)

  13. Cheng J-Y, Huang S-J, Hsieh C-T (2015) Application of Gabor–Wigner transform to inspect high-impedance fault-generated signals. Int J Electr Power Energy Syst 73:192–199

    Article  Google Scholar 

  14. Ghaderi A, Mohammadpour HA, Ginn HL, Shin Y-J (2015) High-impedance fault detection in the distribution network using the time-frequency-based algorithm. IEEE Trans Power Deliv 30(3):1260–1268

    Article  Google Scholar 

  15. Chaitanya BK, Yadav A, Pazoki M (2020) An intelligent detection of high-impedance faults for distribution lines integrated with distributed generators. IEEE Syst J 14(1):870–879

    Article  Google Scholar 

  16. Veerasamy V, Wahab NIA, Ramachandran R, Thirumeni M, Subramanian C, Othman ML, Hizam H (2019) High-impedance fault detection in medium-voltage distribution network using computational intelligence-based classifiers. Neural Comput Appl 31(12):9127–9143

    Article  Google Scholar 

  17. Michalik M, Lukowicz M, Rebizant W, Lee S-J, Kang S-H (2008) New ann-based algorithms for detecting HIFs in multigrounded MV networks. IEEE Trans Power Deliv 23(1):58–66

    Article  Google Scholar 

  18. Baqui I, Zamora I, Mazón J, Buigues G (2011) High impedance fault detection methodology using wavelet transform and artificial neural networks. Electr Power Syst Res 81(7):1325–1333

    Article  Google Scholar 

  19. Fekri MN, Patel H, Grolinger K, Sharma V (2021) Deep learning for load forecasting with smart meter data: online adaptive recurrent neural network. Appl Energy 282:116177

    Article  Google Scholar 

  20. Veerasamy V, Wahab NIA, Othman ML, Padmanaban S, Sekar K, Ramachandran R, Hizam H, Vinayagam A, Islam MZ (2021) LSTM recurrent neural network classifier for high impedance fault detection in solar PV integrated power system. IEEE Access 9:32672–32687

    Article  Google Scholar 

  21. Chakraborty S, Das S (2019) Application of smart meters in high impedance fault detection on distribution systems. IEEE Trans Smart Grid 10(3):3465–3473

    Article  Google Scholar 

  22. Soheili A, Sadeh J (2017) Evidential reasoning based approach to high impedance fault detection in power distribution systems. IET Gener Transm Distrib 11(5):1325–1336

    Article  Google Scholar 

  23. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. In: Adv. Neural Inf. Process. Syst., pp. 5998–6008 (2017)

  24. Rußwurm M, Körner M (2020) Self-attention for raw optical satellite time series classification. ISPRS J Photogramm Remote Sens 169:421–435

    Article  Google Scholar 

  25. Ince T, Kiranyaz S, Eren L, Askar M, Gabbouj M (2016) Real-time motor fault detection by 1-d convolutional neural networks. IEEE Trans Ind Electron 63(11):7067–7075

    Article  Google Scholar 

  26. Gholamiangonabadi D, Kiselov N, Grolinger K (2020) Deep neural networks for human activity recognition with wearable sensors: leave-one-subject-out cross-validation for model selection. IEEE Access 8:133982–133994

    Article  Google Scholar 

  27. Rai, K., Hojatpanah, F., Badrkhani Ajaei, F., Grolinger, K.: Deep learning for high-impedance fault detection: convolutional autoencoders. Energies 14(12) (2021)

  28. Kersting WH (1991) Radial distribution test feeders. IEEE Trans Power Syst 6(3):975–985

    Article  Google Scholar 

  29. Wei M, Shi F, Zhang H, Jin Z, Terzija V, Zhou J, Bao H (2020) High impedance arc fault detection based on the harmonic randomness and waveform distortion in the distribution system. IEEE Trans Power Deliv 35(2):837–850

    Article  Google Scholar 

  30. Santos W, Lopes F, Brito N, Souza B (2017) High-impedance fault identification on distribution networks. IEEE Trans Power Deliv 32(1):23–32

    Article  Google Scholar 

  31. Cui Q, El-Arroudi K, Weng Y (2019) A feature selection method for high impedance fault detection. IEEE Trans Power Deliv 34(3):1203–1215

    Article  Google Scholar 

  32. Lai TM, Snider LA, Lo E, Sutanto D (2005) High-impedance fault detection using discrete wavelet transform and frequency range and RMS conversion. IEEE Trans Power Deliv 20(1):397–407

    Article  Google Scholar 

  33. Biswal M, Ghore S, Malik O, Bansal RC (2021) Development of time-frequency based approach to detect high impedance fault in an inverter interfaced distribution system. IEEE Trans Power Deliv 36(6):3825–3833

    Article  Google Scholar 

  34. Sokolova, M., Japkowicz, N., Szpakowicz, S.: Beyond accuracy, F-score and ROC: a family of discriminant measures for performance evaluation. In: Australasian joint conference on artificial intelligence, pp 1015–1021 (2006)

  35. Narasimhulu N, Kumar DA, Kumar MV (2020) LWT based ANN with ant lion optimizer for detection and classification of high impedance faults in distribution system. J Electr Eng Technol 15:1631–1650

    Article  Google Scholar 

Download references

Funding

This research has been supported by NSERC under grants RGPIN-2018-06222 and RGPIN2017-04772.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Katarina Grolinger.

Ethics declarations

Conflicts of interest:

The authors declare that there is no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Rai, K., Hojatpanah, F., Ajaei, F.B. et al. Deep learning for high-impedance fault detection and classification: transformer-CNN. Neural Comput & Applic (2022). https://doi.org/10.1007/s00521-022-07219-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s00521-022-07219-z

Keywords

  • High-impedance fault detection
  • Deep learning
  • Transformer network
  • Convolutional neural network
  • Power system protection