Skip to main content
SpringerLink
Log in

Classification and Fast Detection of Transmission Line Faults Using Signal Entropy

  • Original Contribution
  • Published:
Journal of The Institution of Engineers (India): Series B Aims and scope Submit manuscript

Abstract

A simple, prompt and accurate method of fault identification and classification of faults in a long transmission system is presented here using entropy analysis of post-fault three-phase current signals, measured at the sending end only. Identification of the faulted phase is imperative for restricting unwanted outage of power through the faulted phase, as well as isolation of the same in order to retain the stability of the system. Transmission line fault classification, hence, has become one of the most vital topics of research. The high-frequency transient current oscillations, appearing immediately after the fault, are analyzed in this work. Signal entropy is computed for the modified waveform of each phase using the differentiation method to highlight the edges of oscillation. Entropy measures the randomness of a signal; hence, the sudden disturbance caused in the directly affected faulted line yields much higher entropy compared to the un-faulted lines. It is further observed that the extent of disturbance in the three phases primarily depends on the type of fault caused: ground fault or non-ground fault. An attempt has been made closely to monitor the entropy levels of the three-phase currents to distinguish between each phase in terms of the level of disturbance caused in a specific class of fault using the estimated signal entropy values, thereby aiding in the development of a threshold-based fault classifier entropy signatures. The major findings of the present work are primarily twofold: 100% accuracy of classification and requirement of only (1/20)th of post-fault signal, that is, detection within 1 ms, which is commendably fast compared to several contemporary works. Besides, the variation of fault parameters, such as fault location, fault resistance and power line noise, makes the design more practically suited.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. A. Prasad, J.B. Edward, K. Ravi, A review on fault classification methodologies in power transmission systems: part—I. J. Electr. Syst. Inf. Technol. 5(1), 48–60 (2018)

    Article  Google Scholar 

  2. D.P. Mishra, P. Ray, Fault detection, location and classification of a transmission line. Neural Comput. Appl. 30(5), 1377–1424 (2017)

    Article  Google Scholar 

  3. N. Roy, K. Bhattacharya, Detection, classification, and estimation of fault location on an overhead transmission line using S-transform and neural network. Electr. Power Compon. Syst. 43(4), 461–472 (2015)

    Article  Google Scholar 

  4. Z. Moravej, J.D. Ashkezari, M. Pazoki, An effective combined method for symmetrical faults identification during power swing. Int. J. Electr. Power Energy Syst. 64, 24–34 (2015)

    Article  Google Scholar 

  5. Y.Q. Chen, O. Fink, G. Sansavini, Combined fault location and classification for power transmission lines fault diagnosis with integrated feature extraction. IEEE Trans. Industr. Electron. 65(1), 561–569 (2017)

    Article  Google Scholar 

  6. S.K. Sharma, GA-GNN (Genetic Algorithm-Generalized Neural Network)-based fault classification system for three-phase transmission system. J. Inst. Eng. India Ser. B 100(5), 435–445 (2019)

    Article  Google Scholar 

  7. S.C. Shekar, G. Kumar, S.V.N.L. Lalitha, A transient current based micro-grid connected power system protection scheme using wavelet approach. Int. J. Electr. Comput. Eng. 9(1), 14 (2019)

    Google Scholar 

  8. S. Devi, N.K. Swarnkar, S.R. Ola, O.P. Mahela, (2016). Detection of transmission line faults using discrete wavelet transform. Paper presented at 2016 Conference on Advances in Signal Processing (CASP). IEEE, pp. 133–138

  9. A. Yadav, A. Swetapadma, A novel transmission line relaying scheme for fault detection and classification using wavelet transform and linear discriminant analysis. Ain Shams Eng. J. 6(1), 199–209 (2015)

    Article  Google Scholar 

  10. A. Yadav, A. Swetapadma, A single ended directional fault section identifier and fault locator for double circuit transmission lines using combined wavelet and ANN approach. Int. J. Electr. Power Energy Syst. 69, 27–33 (2015)

    Article  Google Scholar 

  11. A. Dasgupta, S. Nath, A. Das, Transmission line fault classification and location using wavelet entropy and neural network. Electr. Power Compon. Syst. 40(15), 1676–1689 (2012)

    Article  Google Scholar 

  12. B.Y. Vyas, R.P. Maheshwari, B. Das, Improved fault analysis technique for protection of thyristor controlled series compensated transmission line. Int. J. Electr. Power Energy Syst. 55, 321–330 (2014)

    Article  Google Scholar 

  13. Z. Jiao, R. Wu, A new method to improve fault location accuracy in transmission line based on fuzzy multi-sensor data fusion. IEEE Trans. Smart Grid 10(4), 4211–4220 (2018)

    Article  MathSciNet  Google Scholar 

  14. A. Yadav, A. Swetapadma, Enhancing the performance of transmission line directional relaying, fault classification and fault location schemes using fuzzy inference system. IET Gener. Transm. Distrib. 9(6), 580–591 (2015)

    Article  Google Scholar 

  15. R. Goli, A.G. Shaik, S.S.T. Ram, Fuzzy-wavelet based double line transmission system protection scheme in the presence of SVC. J. Inst. Eng. India Ser. B 96(2), 131–140 (2015)

    Article  Google Scholar 

  16. M.J. Reddy, D.K. Mohanta, A wavelet-neuro-fuzzy combined approach for digital relaying of transmission line faults. Elect. Power Compon. Syst. 35(12), 1385–1407 (2007)

    Article  Google Scholar 

  17. A. Khaleghi, M.O. Sadegh, Single-phase fault location in four-circuit transmission lines based on wavelet analysis using ANFIS. J. Electr. Eng. Technol. 14(4), 1577–1584 (2019)

    Article  Google Scholar 

  18. B.Y. Vyas, R.P. Maheshwari, B. Das, Pattern recognition application of support vector machine for fault classification of thyristor controlled series compensated transmission lines. J. Inst. Eng. India Ser. B 97(2), 175–183 (2016)

    Article  Google Scholar 

  19. B. Bhalja, R.P. Maheshwari, Wavelet-based fault classification scheme for a transmission line using a support vector machine. Electr. Power Compon. Syst. 36(10), 1017–1030 (2008)

    Article  Google Scholar 

  20. S.R. Samantaray, P.K. Dash, G. Panda, Distance relaying for transmission line using support vector machine and radial basis function neural network. Int. J. Electr. Power Energy Syst. 29(7), 551–556 (2007)

    Article  Google Scholar 

  21. M.J.B. Reddy, P. Gopakumar, D.K. Mohanta, A novel transmission line protection using DOST and SVM. Eng. Sci. Technol. Int. J. 19(2), 1027–1039 (2016)

    Google Scholar 

  22. B. Patel, A new FDOST entropy based intelligent digital relaying for detection, classification and localization of faults on the hybrid transmission line. Electr. Power Syst. Res. 157, 39–47 (2018)

    Article  Google Scholar 

  23. S. Hasheminejad, S.G. Seifossadat, M. Razaz, M. Joorabian, Traveling-wave-based protection of parallel transmission lines using Teager energy operator and fuzzy systems. IET Gener. Transm. Distrib. 10(4), 1067–1074 (2016)

    Article  Google Scholar 

  24. F.V. Lopes, K.M. Dantas, K.M. Silva, F.B. Costa, Accurate two-terminal transmission line fault location using traveling waves. IEEE Trans. Power Delivery 33(2), 873–880 (2017)

    Article  Google Scholar 

  25. G. Ma, L. Jiang, K. Zhou, G. Xu, A Method of line fault location based on traveling wave theory. Int. J. Control Autom. 9(2), 261–270 (2016)

    Article  Google Scholar 

  26. A. Maheshwari, V. Agarwal, S.K. Sharma, Comparative analysis of ANN-based FL and travelling wave-based FL for location of fault on transmission lines. J. Inst. Eng. India Ser. B 100, 1–10 (2019)

    Article  Google Scholar 

  27. A. Mukherjee, P. Kundu, A. Das, Identification and classification of power system faults using ratio analysis of principal component distances. Indones. J. Electr. Eng. Comput. Sci. 12(11), 7603–7612 (2014)

    Google Scholar 

  28. Alsafasfeh, Q., Abdel-Qader, I., & Harb, A. (2010, May). Symmetrical pattern and PCA based framework for fault detection and classification in power systems. Paper presented at 2010 IEEE international conference on electro/information technology. pp. 1–5

  29. A. Mukherjee, P.K. Kundu, A. Das, Power system fault identification and localization using multiple linear regression of principal component distance indices. Int. J. Appl. Power Eng. 9(2), 113–126 (2020)

    Google Scholar 

  30. A. Mukherjee, P.K. Kundu, A. Das, Application of principal component analysis for fault classification in transmission line with ratio-based method and probabilistic neural network: a comparative analysis. J. Inst. Eng. India Ser. B 101(4), 321–333 (2020)

    Article  Google Scholar 

  31. P. Jafarian, M. Sanaye-Pasand, A traveling-wave-based protection technique using wavelet/PCA analysis. IEEE Trans. Power Deliv. 25(2), 588–599 (2010)

    Article  Google Scholar 

  32. Y. Guo, K. Li, X. Liu, Fault diagnosis for power system transmission line based on PCA and SVMs, in International Conference on Intelligent Computing for Sustainable Energy and Environment. (Springer, Berlin, Heidelberg, 2012), pp. 524–532

    Google Scholar 

  33. J.A. Jiang, C.L. Chuang, Y.C. Wang, C.H. Hung, J.Y. Wang, C.H. Lee, Y.T. Hsiao, A hybrid framework for fault detection, classification, and location—part II: implementation and test results. IEEE Trans. Power Deliv. 26(3), 1999–2008 (2011)

    Article  Google Scholar 

  34. V.K. Gaur, B. Bhalja, A new faulty section identification and fault localization technique for three-terminal transmission line. Int. J. Electr. Power Energy Syst. 93, 216–227 (2017)

    Article  Google Scholar 

  35. S. Barman, B.K.S. Roy, Detection and location of faults in large transmission networks using minimum number of phasor measurement units. IET Gener. Transm. Distrib. 12(8), 1941–1950 (2018)

    Article  Google Scholar 

  36. M.M. Devi, M. Geethanjali, A.R. Devi, Fault localization for transmission lines with optimal phasor measurement units. Comput. Electr. Eng. 70, 163–178 (2018)

    Article  Google Scholar 

  37. L. Ji, X. Tao, Y. Fu, Y. Fu, Y. Mi, Z. Li, A new single ended fault location method for transmission line based on positive sequence superimposed network during auto-reclosing. IEEE Trans. Power Deliv. 34(3), 1019–1029 (2019)

    Google Scholar 

  38. A. Ghorbani, H. Mehrjerdi, Negative-sequence network based fault location scheme for double-circuit multi-terminal transmission lines. IEEE Trans. Power Deliv. 34(3), 1109–1117 (2019)

    Article  Google Scholar 

  39. R. Godse, S. Bhat, Mathematical morphology-based feature-extraction technique for detection and classification of faults on power transmission line. IEEE Access 8, 38459–38471 (2020)

    Article  Google Scholar 

  40. R. Fan, Y. Liu, R. Huang, R. Diao, S. Wang, Precise fault location on transmission lines using ensemble Kalman filter. IEEE Trans. Power Deliv. 33(6), 3252–3255 (2018)

    Article  Google Scholar 

  41. H. Shu, N. An, B. Yang, Y. Dai, Y. Guo, Single pole-to-ground fault analysis of MMC-HVDC transmission lines based on capacitive fuzzy identification algorithm. Energies 13(2), 319 (2020)

    Article  Google Scholar 

  42. A. Codino, Z. Wang, R. Razzaghi, M. Paolone, F. Rachidi, An alternative method for locating faults in transmission line networks based on time reversal. IEEE Trans. Electromagn. Compat. 59(5), 1601–1612 (2017)

    Article  Google Scholar 

  43. A. Swetapadma, A. Yadav, Data-mining-based fault during power swing identification in power transmission system. IET Sci. Meas. Technol. 10(2), 130–139 (2016)

    Article  Google Scholar 

  44. Y. Liu, A.S. Meliopoulos, Z. Tan, L. Sun, R. Fan, Dynamic state estimation-based fault locating on transmission lines. IET Gener. Transm. Distrib. 11(17), 4184–4192 (2017)

    Article  Google Scholar 

  45. A.R. Almeida, O.M. Almeida, B.F.S. Junior, L.H.S.C. Barreto, A.K. Barros, ICA feature extraction for the location and classification of faults in high-voltage transmission lines. Electr. Power Syst. Res. 148, 254–263 (2017)

    Article  Google Scholar 

  46. A. Khoshnami, I. Sadeghkhani, Sample entropy-based fault detection for photovoltaic arrays. IET Renew. Power Gener. 12(16), 1966–1976 (2018)

    Article  Google Scholar 

  47. S. Ekici, S. Yildirim, M. Poyraz, Energy and entropy-based feature extraction for locating fault on transmission lines by using neural network and wavelet packet decomposition. Expert Syst. Appl. 34(4), 2937–2944 (2008)

    Article  Google Scholar 

  48. A.V. Oppenheim, R.W. Schafer, Discrete-Time Signal Processing (Prentice-Hall, Englewood Cliffs, NJ, 1989), pp. 311–312

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Government College of Engineering and Ceramic Technology, Kolkata, 700010, India

    Alok Mukherjee

  2. Department of Electrical Engineering, Jadavpur University, Kolkata, 700032, India

    Palash Kumar Kundu & Arabinda Das

Authors
  1. Alok Mukherjee
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Palash Kumar Kundu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Arabinda Das
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Arabinda Das.

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

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mukherjee, A., Kundu, P.K. & Das, A. Classification and Fast Detection of Transmission Line Faults Using Signal Entropy. J. Inst. Eng. India Ser. B 102, 655–670 (2021). https://doi.org/10.1007/s40031-020-00526-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40031-020-00526-w

Keywords

Search

Navigation