Skip to main content

Advertisement

Springer Nature Link
Log in

A comprehensive evaluation of multicategory classification methods for fault classification in series compensated transmission line

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

This paper presents a new method for fault classification in series-compensated transmission line using multiclass support vector machine (MCSVM) and multi class extreme learning machine (MCELM). These methods use the information obtained from the wavelet decomposition of fault current signals for fault classification. Using MATLAB simulink, data set has been generated with different types of fault and system variables, which include the fault resistance, fault distance, load angle and fault inception angle. The proposed method has been tested on a 400-kV, 300-km transmission line under variety of fault conditions. The performance of MCSVM and MCELM is compared in terms of training time and classification accuracy. The comparisons have been made for both One-Versus-One and One-Versus-Rest methods of SVMs and ELMs. Results show that MCELMs need less training time compared to MCSVMs, and the classification accuracy of MCELMs is more or less similar to MCSVMs. The feasibility of the proposed methods is also tested on a practical 220-kV series-compensated transmission line, and the results obtained are quite promising.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Similar content being viewed by others

Explore related subjects

Discover the latest articles and news from researchers in related subjects, suggested using machine learning.

References

  1. Saha MM et al (1999) A new accurate fault location algorithm for series compensated lines. IEEE Trans Power Deliv 14:789–797

    Article  MathSciNet  Google Scholar 

  2. Phadke AG, Thorp JS (1988) Computer relaying for power systems. Wiley, Taunton

    Google Scholar 

  3. Girgis AA, Sallam AA, Karim El-Din A (1998) An adaptive protection scheme for advanced series compensated transmission lines. IEEE Trans Power Deliv 13:414–420

    Article  Google Scholar 

  4. Pradhan AK, Routray A, Biswal B (2004) Higher order statistics-Fuzzy integrated scheme for fault classification of a series compensated line. IEEE Trans Power Deliv 19:891–893

    Article  Google Scholar 

  5. Pradhan AK, Routray A, Pati S, Pradhan DK (2005) Wavelet Fuzzy combined approach for fault classification of a series compensated transmission line. IEEE Trans Power Deliv 19:1612–1618

    Article  Google Scholar 

  6. Biswarup D, Vittal Reddy J (2005) Fuzzy-logic-based fault classification scheme for digital distance protection. IEEE Trans Power Deliv 20:609–616

    Article  Google Scholar 

  7. Abdelaziz AY, Ibrahim AM, Mansour MM, Talaat HE (2005) Modern approaches for protection of series compensated transmission lines. Electr Power Syst Res 75:85–98

    Article  Google Scholar 

  8. Dash PK, Pradhan AK, Panda G (2003) Application of artificial intelligence techniques for fault classification and location of faults on Thyristor controlled series compensated line. Electr Power Compon Syst 31:241–260

    Article  Google Scholar 

  9. Martin F, Aguado JA (2003) Wavelet based ANN approach for transmission line protection. IEEE Trans Power Deliv 186:1572–1574

    Article  Google Scholar 

  10. Nan Z, Mladen K (2007) Transmission line boundary protection using wavelet transform and neural network. IEEE Trans Power Deliv 22:859–869

    Article  Google Scholar 

  11. Urmil BP, Bhavesh RB, Rudra PM, Biswarup D (2007) Decision tree based fault classification scheme for protection of series compensated transmission line. Int J Emerg Electr Power Syst 8:1–6

    Google Scholar 

  12. Dash PK, Samantaray SR, Panda Ganapathy (2007) Fault classification and section identification of an advanced series compensated transmission line using support vector machine. IEEE Trans Power Deliv 22:67–73

    Article  Google Scholar 

  13. Urmil BP, Biswarup D, Rudra PM (2008) Combined wavelet-SVM technique for fault zone detection in a series compensated transmission line. IEEE Trans Power Deliv 23:1789–1794

    Article  Google Scholar 

  14. Cortes C, Vapnik VN (1995) Support vector networks. Mach Learn 20:273–297

    MATH  Google Scholar 

  15. Vapnik VN (1995) The nature of statistical learning theory. Springer, Berlin

  16. Vapnik VN (1998) The statistical learning theory. Wiley, New York

    Google Scholar 

  17. Guang-Bin H, Qin-Yu Z, Chee-Kheong S (2006) Extreme learning machine: theory and applications. Neurocomputing 70:489–501

    Article  Google Scholar 

  18. Nan-Ying L, Paramasivam S, Guang-Bin H (2006) Classification of mental tasks from EEG signals using extreme learning machine. Int J Neural Syst 16:29–38

    Google Scholar 

  19. http://www.isis.ecs.soton.ac.uk/resources/svminfo/

  20. http://www.ntu.edu.sg/home/egbhuang/

Download references

Author information

Authors and Affiliations

  1. Raja College of Engineering and Technology, Madurai, 625 020, Tamil Nadu, India

    V. Malathi

  2. National Engineering College, Kovilpatti, Tamil Nadu, India

    N. S. Marimuthu

  3. Thiagarajar College of Engineering, Madurai, Tamil Nadu, India

    S. Baskar

Authors
  1. V. Malathi
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. N. S. Marimuthu
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. S. Baskar
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to V. Malathi.

Appendix 1

Appendix 1

  • Source data at both sending and receiving ends of the line:

  • Positive sequence impedance: 1.31 + j15.0 Ω.

  • Zero sequence impedance: 2.33 + j26.6 Ω.

  • System frequency: 50 Hz.

  • Transmission line data:

  • Length of the line: 300 km

  • Voltage: 400 kV

  • Positive sequence impedance: 8.25 + j94.5 Ω

  • Zero sequence impedance: 82.5 + j308 Ω

  • Positive sequence capacitance: 13 nF/km.

  • Zero sequence capacitance: 8.5 nF/km.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Malathi, V., Marimuthu, N.S. & Baskar, S. A comprehensive evaluation of multicategory classification methods for fault classification in series compensated transmission line. Neural Comput & Applic 19, 595–600 (2010). https://doi.org/10.1007/s00521-009-0312-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-009-0312-9

Keywords