Intelligent schemes for fault classification in mutually coupled series-compensated parallel transmission lines

  • Aleena Swetapadma
  • Anamika YadavEmail author
  • Almoataz Y. Abdelaziz
Original Article


The protection of mutually coupled series capacitor-compensated (SCC) parallel transmission lines is a more complicated task than uncompensated lines due to the effect of mutual coupling, inter-circuit faults, and non-linearity of effective impedance of SCC line. A method that can overcome these issues and still work efficiently is a supervised learning-based method which is an adaptive technique. Hence, in this work, various supervised learning-based intelligent schemes like artificial neural network (ANN), support vector machines (SVM), and decision tree (DT) are employed to find a suitable method for the protection of series capacitor-compensated lines. Discrete wavelet transform has been used to process the three-phase current signals of the parallel lines measured at one terminal only. A moving window of 20 samples is selected, and approximate wavelet coefficient is calculated up to level 1 using DB-4 mother wavelet. The resultant is then given as input to the intelligent schemes (ANN, SVM, and DT). The proposed intelligent schemes have been tested with variety of fault conditions such as inter-circuit faults, cross-country faults, transforming faults, single-circuit operation, and high resistance faults. A large number of fault simulation studies corroborate that DT-based fault classification method is better than ANN and SVM. The accuracy of faulty phase and ground identification scheme is 100% for all the tested fault cases. Hence, the proposed supervised learning-based intelligent method can be implemented in real power system network effectively.


Artificial neural network Series compensation Mutual coupling Fault phase identification Fault classification Decision tree Support vector machine 



Artificial neural network


Phase A of circuit 1


Phase A of circuit 2


Phase B of circuit 1


Phase B of circuit 2


Phase C of circuit 1


Phase C of circuit 2


Discrete wavelet transform


Decision tree


Faulty phase identification


Ground identification


Circuit 1 grounded


Circuit 2 grounded


Wavelet-processed current of phase A of circuit 1


Wavelet-processed current of phase B of circuit 1


Wavelet-processed current of phase C of circuit 1


Wavelet-processed current of phase A of circuit 2


Wavelet-processed current of phase B of circuit 2


Wavelet-processed current of phase C of circuit 2


Zero sequence current of circuit 1


Zero sequence current of circuit 2


Not grounded


Series capacitor compensated


Support vector machine


Wavelet transform


Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.


  1. 1.
    Hingorani NG, Gyugyi L (2001) Understanding FACTS, First Indian edition, IEEE PressGoogle Scholar
  2. 2.
    Yadav A, Swetapadma A (2014) Improved first zone reach setting of artificial neural network-based directional relay for protection of double circuit transmission lines. IET Gener Transm Distrib 8:373–388CrossRefGoogle Scholar
  3. 3.
    Swetapadma Aleena, Yadav Anamika (2016) Directional relaying using support vector machine for double circuit transmission lines including cross-country and inter-circuit faults. Int J Electr Power Energy Syst 81:254–264CrossRefGoogle Scholar
  4. 4.
    Swetapadma Aleena, Yadav Anamika (2017) A novel decision tree regression based fault distance estimation scheme for transmission lines. IEEE Trans Power Deliv 32(1):234–245CrossRefGoogle Scholar
  5. 5.
    Yadav A, Swetapadma A (2016) A finite-state machine based approach for fault detection and classification in transmission lines. Electr Power Compon Syst 44(1):43–59CrossRefGoogle Scholar
  6. 6.
    Krishnanand KR, Dash PK, Naeem MH (2015) Detection, classification, and location of faults in power transmission lines. Int J Electr Power Energy Syst 31(67):76–86CrossRefGoogle Scholar
  7. 7.
    Megahed AI, Moussa AM, Bayoumy AE (2006) Usage of wavelet transform in the protection of series compensated transmission lines. IEEE Trans Power Deliv 21:1213–1221CrossRefGoogle Scholar
  8. 8.
    Dash PK, Samantray SR (2004) Phase selection and fault section identification in thyristor controlled series compensated line using discrete wavelet transform. Int J Electr Power Energy Syst 26:725–732CrossRefGoogle Scholar
  9. 9.
    Abdelaziz AY, Ibrahim AM, Mansour MM, Talaat HE (2005) Modern approaches for protection of series compensated transmission lines. Electr Power Syst Res 75(1):85–98CrossRefGoogle Scholar
  10. 10.
    Song YH, Johns AT, Xuan QY (1996) Artificial neural-network-based protection scheme for controllable series-compensated EHV transmission lines. IEE Proc Gener Transm Distrib 143:535–540CrossRefGoogle Scholar
  11. 11.
    Song YH, Johns AT, Xuan QY (1997) Protection scheme for EHV transmission systems with thyristor controlled series compensation using radial basis function neural networks. Electr Mach Power Syst 25:553–565CrossRefGoogle Scholar
  12. 12.
    Parikh UB, Das B, Maheshwari R (2010) Fault classification technique for series compensated transmission line using support vector machine. Int J Electr Power Energy Syst 32:629–636CrossRefGoogle Scholar
  13. 13.
    Dash P, Samantaray S, Panda G (2007) Fault classification and section identification of an advanced series compensated transmission line using support vector machine. IEEE Trans Power Deliv 22:67–73CrossRefGoogle Scholar
  14. 14.
    Malathi V, Marimuthu NS, Baskar S, Ramar K (2011) Application of extreme learning machine for series compensated transmission line protection. Eng Appl Art Int 24:880–887CrossRefGoogle Scholar
  15. 15.
    Pradhan AK, Routray A, Pati S, Pradhan DK (2004) Wavelet fuzzy combined approach for fault classification of a series-compensated transmission line. IEEE Trans Power Deliv 19:1612–1618CrossRefGoogle Scholar
  16. 16.
    Eristi H (2013) Fault diagnosis system for series compensated transmission line based on wavelet transform and adaptive neuro-fuzzy inference system. Measurement 46:393–401CrossRefGoogle Scholar
  17. 17.
    Raval PD, Pandya AS (2016) Improved fault classification in series compensated EHV transmission line using wavelet transform and artificial neural network. In: 2016 IEEE 1st international conference on power electronics, intelligent control and energy systems (ICPEICES), Delhi, pp 1–5Google Scholar
  18. 18.
    Samantaray SR (2007) Decision tree based fault zone identification and fault classification in flexible AC transmission based transmission line. IET-Gen Trans Distrib 3:425–436CrossRefGoogle Scholar
  19. 19.
    Jamehbozorg A, Shahrtash SM (2010) A decision tree based method for fault classification in a single circuit transmission line. IEEE Trans Power Deliv 25:2190–2196CrossRefGoogle Scholar
  20. 20.
    Abdelaziz AY, Mekhamer SF, Ezzat M (2013) Fault location of uncompensated/series compensated lines using two-ends synchronized measurements. Electr Power Compon Syst 41(7):693–715CrossRefGoogle Scholar
  21. 21.
    Raval PD, Pandya AS (2017) A hybrid Wavelet-ANN protection scheme for series compensated EHV transmission line. J Intell Fuzzy Syst 32(4):3051–3058CrossRefGoogle Scholar
  22. 22.
    Han J, Kamber M (2006) Data mining: concepts and techniques, 2nd edn. Morgan Kaufmann Publishers, BurlingtonzbMATHGoogle Scholar
  23. 23.
    Shawe-Taylor J, Cristianni N (2000) Support vector machines and other kernel based learning methods. Cambridge University Press, CambridgeGoogle Scholar
  24. 24.
    Hagan MH, Demuth HB, Beale MH (1996) Neural network design. PWS Publishing Co. Boston, ISBN: 0-534-94332-2Google Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Computer EngineeringKIIT UniversityBhubaneswarIndia
  2. 2.Department of Electrical EngineeringNational Institute of TechnologyRaipurIndia
  3. 3.Faculty of Engineering and TechnologyFuture University in EgyptCairoEgypt

Personalised recommendations