Skip to main content
SpringerLink
Log in

Application of Poincaré analogous time-split signal-based statistical correlation for transmission line fault classification

  • Original Paper
  • Published:
Electrical Engineering Aims and scope Submit manuscript

Abstract

A transmission line fault classification scheme is proposed in this article using Poincaré-based correlation analysis of three-phase fault currents. The method segments each fault signal into two equal time-split components and computes correlation coefficient between these two time-split signals. The fault current signals of the directly affected line(s) observe an abrupt monotonic rise, compared to the indirectly affected phases. This sudden rise in magnitude is expressed with correlation coefficients between the two almost consecutive time-split components of signal, time shifted by delay index. This method emphasizes this monotonic nature of increment of the fault current, enabling prompt fault detection. Further analysis of three phases of fault signals independently yields a set of correlation coefficients for ten different fault prototypes, which are used to develop fault classifier signatures for direct classification. The proposed method yields high classification accuracy of 99.76% using only (1/6)th of the post-fault noisy signal with fault resistance varying from 0.01 to 100Ω. Besides, analysis of only one end single discards the requirement of time synchronous signal acquisition from both ends. Finally, use of simple analysis reduces the computational burden compared to several contemporary methods.

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.

Institutional subscriptions

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

Similar content being viewed by others

References

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

    Article  Google Scholar 

  2. Mukherjee A, Kundu PK, Das A (2021) Transmission line faults in power system and the different algorithms for identification classification and localization: a brief review of methods. J Inst Eng India Ser B. https://doi.org/10.1007/s40031-020-00530-0

    Article  Google Scholar 

  3. Mourot L, Bouhaddi M, Perrey S, Rouillon JD, Regnard J (2004) Quantitative Poincare plot analysis of heart rate variability: effect of endurance training. Eur J Appl Physiol 91(1):79–87

    Article  Google Scholar 

  4. Park J, Lee S, Jeon M (2009) Atrial fibrillation detection by heart rate variability in Poincare plot. Biomed Eng Online 8(1):38

    Article  Google Scholar 

  5. Chatterjee B, Debnath S (2020) Cross correlation aided fuzzy based relaying scheme for fault classification in transmission lines. Eng Sci Technol Int J 23(3):534–543

    Google Scholar 

  6. Lei A, Dong X, Terzija V (2018) An ultra-high-speed directional relay based on correlation of incremental quantities. IEEE Trans Power Delivery 33(6):2726–2735

    Article  Google Scholar 

  7. Zhang G, Shu H, Liao Y (2016) Automated double-ended traveling wave record correlation for transmission line disturbance analysis. Electric Power Syst Res 136:242–250

    Article  Google Scholar 

  8. Y. J. Kwon, S. H. Kang, D. G. Lee, H. K. Kim, Fault location algorithm based on cross correlation method for HVDC cable lines. In:IET 9th International Conference on Developments in Power Systems Protection (DPSP 2008), 2008, pp. 360–364.

  9. A. Bhattacharjee, A. Dasgupta, Cross-correlation based distance estimation of single line to ground faults using Elman back-propagation neural network. In: 3rd International conference on Electronics, Communication and Aerospace Technology (ICECA) IEEE, 2019, pp. 1230–1235.

  10. Dasgupta A, Debnath S, Das A (2015) Transmission line fault detection and classification using cross-correlation and k-nearest neighbor. Int J Knowl Based Intell Eng Syst 19(3):183–189

    Google Scholar 

  11. Musa MH, He Z, Fu L, Deng Y (2018) A correlation coefficient-based algorithm for fault detection and classification in a power transmission line. IEEJ Trans Electr Electron Eng 13(10):1394–1403

    Article  Google Scholar 

  12. C. Yu-Wu, G. Yu-Hong, Fault phase selection for transmission line based on correlation coefficient. In:International Conference on Computer Application and System Modeling (ICCASM 2010) IEEE, 2010, pp. 350–354.

  13. Zhu K, Lee WK, Pong PW (2018) Fault-line identification of HVDC transmission lines by frequency-spectrum correlation based on capacitive coupling and magnetic field sensing. IEEE Trans Magn 54(11):1–5

    Google Scholar 

  14. Lin XN, Zhao M, Alymann K, Liu P (2005) Novel design of a fast phase selector using correlation analysis. IEEE Trans Power Delivery 20(2):1283–1290

    Article  Google Scholar 

  15. Ghaffarzadeh N (2013) A new method for recognition of arcing faults in transmission lines using wavelet transform and correlation coefficient. Indonesian J Electr Eng Inf 1(1):1–7

    Google Scholar 

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

    Article  Google Scholar 

  17. Ngaopitakkul A, Leelajindakrairerk M (2018) Application of probabilistic neural network with transmission and distribution protection schemes for classification of fault types on radial, loop, and underground structures. Electr Eng 100(2):461–479

    Article  Google Scholar 

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

    Article  Google Scholar 

  19. Shekar SC, Kumar G, Lalitha SVNL (2019) A transient current based micro-grid connected power system protection scheme using wavelet approach. Int J Electr Comput Eng 9(1):14

    Google Scholar 

  20. Yadav A, Swetapadma A (2015) 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

    Article  Google Scholar 

  21. Yadav A, Swetapadma A (2015) 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

    Article  Google Scholar 

  22. Vyas BY, Maheshwari RP, Das B (2014) Improved fault analysis technique for protection of Thyristor controlled series compensated transmission line. Int J Electr Power Energy Syst 55:321–330

    Article  Google Scholar 

  23. Jiao Z, Wu R (2018) 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

    Article  MathSciNet  Google Scholar 

  24. Eristi H (2013) Fault diagnosis system for series compensated transmission line based on wavelet transform and adaptive neuro-fuzzy inference system. Measurement 46(1):393–401

    Article  Google Scholar 

  25. Ekici S (2012) Support Vector Machines for classification and locating faults on transmission lines. Appl Soft Comput 12(6):1650–1658

    Article  Google Scholar 

  26. Koley E, Shukla SK, Ghosh S, Mohanta DK (2017) Protection scheme for power transmission lines based on SVM and ANN considering the presence of non-linear loads. IET Gener Transm Distrib 11(9):2333–2341

    Article  Google Scholar 

  27. Bhalja B, Maheshwari RP (2008) Wavelet-based fault classification scheme for a transmission line using a support vector machine. Electric Power Comp Syst 36(10):1017–1030

    Article  Google Scholar 

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

    Google Scholar 

  29. Mukherjee A, Kundu PK, Das A (2021) A supervised principal component analysis-based approach of fault localization in transmission lines for single line to ground faults. Electr Eng. https://doi.org/10.1007/s00202-021-01221-9

    Article  Google Scholar 

  30. Mukherjee A, Kundu PK (2021) & Das, A Classification and localization of transmission line faults using curve fitting technique with Principal Component Analysis features. Electr Eng. https://doi.org/10.1007/s00202-021-01285-7

    Article  Google Scholar 

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

    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, 2012, pp. 524–532.

  33. Mukherjee A, Kundu PK, Das A (2020) 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

    Article  Google Scholar 

  34. Almeida AR, Almeida OM, Junior BFS, Barreto LHSC, Barros AK (2017) ICA feature extraction for the location and classification of faults in high-voltage transmission lines. Electric Power Syst Res 148:254–263

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  38. Dutta P, Esmaeilian A, Kezunovic M (2014) Transmission-line fault analysis using synchronized sampling. IEEE Trans Power Delivery 29(2):942–950

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Google Scholar 

  47. Jiang JA, Chuang CL, Wang YC, Hung CH, Wang JY, Lee CH, Hsiao YT (2011) A hybrid framework for fault detection, classification, and location—part II: implementation and test results. IEEE Trans Power Delivery 26(3):1999–2008

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

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

    Alok Mukherjee

  2. Computer Science and Engineering, Government College of Engineering and Ceramic Technology, Kolkata, India

    Kingshuk Chatterjee

  3. Electrical Engineering, Jadavpur University, Kolkata, 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. Kingshuk Chatterjee
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Palash Kumar Kundu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. 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., Chatterjee, K., Kundu, P.K. et al. Application of Poincaré analogous time-split signal-based statistical correlation for transmission line fault classification. Electr Eng 104, 1057–1075 (2022). https://doi.org/10.1007/s00202-021-01369-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00202-021-01369-4

Keywords

Search

Navigation