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Neural Computing and Applications

, Volume 30, Issue 3, pp 891–904 | Cite as

Fault analysis in TCSC-compensated lines using wavelets and a PNN

  • E. Reyes-Archundia
  • J. L. Guardado
  • J. A. Gutiérrez-Gnecchi
  • E. L. Moreno-Goytia
  • N. F. Guerrero-Rodriguez
Original Article
  • 146 Downloads

Abstract

This paper describes an algorithm to detect, localize and classify fault events in overhead transmission lines compensated with a thyristor-controlled series capacitor (TCSC). During a fault event, a complex pattern of traveling wave reflections and refractions is generated at the point of fault inception. The proposed algorithm uses the discrete wavelet transform combined with a probabilistic neural network to analyze all this information and determine whether a fault condition exists in the line, the fault type and also the fault distance. In order to assess the algorithm performance, several studies were carried out under varied conditions. The obtained results demonstrate that the algorithm accuracy for calculating the fault distance is smaller than 1% of the total line length, and a 100% efficiency for determining the fault type. The algorithm is also immune to harmonic interaction due to low-frequency harmonics generated by the TCSC. A comparative advantage over previous algorithms for TCSC-compensated transmission lines is the fact that the proposed algorithm not only identifies the faulted line section but also localizes accurately the distance to the fault, using only measurements at one end of the line.

Keywords

Fault localization Discrete wavelet transform Traveling waves Probabilistic neural network Thyristor-controlled series capacitor 

Notes

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

References

  1. 1.
    Dosoglu MK, Arsoy AB, Guvenc U (2016) Application of STATCOM-supercapacitor for low-voltage ride-through capability in DFIG-based wind farm. Neural Comput Appl. doi: 10.1007/s00521-016-2219-6 Google Scholar
  2. 2.
    Adrees A, Milanović JV (2016) Optimal Compensation of Transmission Lines Based on Minimisation of the Risk of Subsynchronous Resonance. IEEE Trans Power Syst 31(2):1038–1047. doi: 10.1109/TPWRS.2015.2422775 CrossRefGoogle Scholar
  3. 3.
    Naresh G, Ramalinga Raju M, Narasimham SVL (2016) Coordinated design of power system stabilizers and TCSC employing improved harmony search algorithm. Swarm Evol Comput 27:169–179. doi: 10.1016/j.swevo.2015.11.003 CrossRefGoogle Scholar
  4. 4.
    Rezaee-Jordehi A (2015) Optimal setting of TCSCs in power systems using teaching–learning-based optimization algorithm. Neural Comput Appl 26(5):1249–1256. doi: 10.1007/s00521-014-1791-x CrossRefGoogle Scholar
  5. 5.
    Shrivastava NA, Panigrahi BK, Meng-Hiot L (2016) Electricity price classification using extreme learning machines. Neural Comput Appl 27(1):9–18. doi: 10.1007/s00521-013-1537-1 CrossRefGoogle Scholar
  6. 6.
    Dubey R, Samantaray SR, Panigrahi BK (2016) Adaptive distance protection scheme for shunt-FACTS compensated line connecting wind farm. IET Gener Transm Distrib 10(1):247–256. doi: 10.1049/iet-gtd.2015.0775 CrossRefGoogle Scholar
  7. 7.
    Cormane J, Nascimento FA (2016) Spectral shape estimation in data compression for smart grid monitoring. IEEE Trans Smart Grid 7(3):1214–1221. doi: 10.1109/TSG.2015.2500359 CrossRefGoogle Scholar
  8. 8.
    Islam B, Baharudin Z, Nallagownden P (2016) Development of chaotically improved meta-heuristics and modified BP neural network-based model for electrical energy demand prediction in smart grid. Neural Comput Appl 27:1–15. doi: 10.1007/s00521-016-2408-3 Google Scholar
  9. 9.
    Yao Y, He X, Huang T, Li C, Xia D (2016) A projection neural network for optimal demand response in smart grid environment. Neural Comput Appl 27:1–9. doi: 10.1007/s00521-016-2532-0 Google Scholar
  10. 10.
    Somayajula D, Crow ML (2015) An integrated dynamic voltage restorer-ultracapacitor design for improving power quality of the distribution grid. IEEE Trans Sustain Energy 6(2):616–624. doi: 10.1109/TSTE.2015.2402221 CrossRefGoogle Scholar
  11. 11.
    Mohammadi MB, Rahmat-Allah H, Fesharaki FH (2016) A new approach for optimal placement of PMUS and their required communication infrastructure in order to minimize the cost of the WAMS. IEEE Trans Smart Grid 7(1):84–93. doi: 10.1109/TSG.2015.2404855 CrossRefGoogle Scholar
  12. 12.
    Abdollahzadeh H, Mozafari B, Jazaeri M (2016) Realistic insights into impedance seen by distance relays of a SSSC-compensated transmission line incorporating shunt capacitance of line. Int J Electr Power Energy Syst 65(1):394–407. doi: 10.1016/j.ijepes.2014.10.037 Google Scholar
  13. 13.
    Manori A, Tripathy M, H O Gupta HO (2016) Investigation of an Advanced Compensated Mho Relay on Double Circuit Series Compensated Transmission Line. TENCON—2015 IEEE Region 10 Conference. doi: 10.1109/TENCON.2015.7372936
  14. 14.
    Zellagui M, Chaghi A (2013) Impact of TCSC on Distance Protection Setting based Modified Particle Swarm Optimization Techniques. I.J. Intell Syst Appl 06:12–24. doi: 10.5815/ijisa.2013.06.02 Google Scholar
  15. 15.
    Dash PK, Samantaray 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(9):725–732. doi: 10.1016/j.ijepes.2004.05.005 CrossRefGoogle Scholar
  16. 16.
    Dash PK, Samantaray SR, Panda G (2007) Fault classification and section identification of an advanced series-compensated transmission line using support vector machine. IEEE Trans Power Deliv 22:67–73. doi: 10.1109/TPWRD.2006.876695 CrossRefGoogle Scholar
  17. 17.
    Samantaray SR (2008) Decision tree-based fault zone identification and fault classification in flexible AC transmissions-based transmission line. IET Gener Transm Distrib 3:425–436. doi: 10.1049/iet-gtd.2008.0316 CrossRefGoogle Scholar
  18. 18.
    El-Zonkoly AM, Desouki H (2011) Wavelet entropy based algorithm for fault detection and classification in FACTS compensated transmission line. Int J Electr Power Energy Syst 33(8):1368–1374. doi: 10.1016/j.ijepes.2011.06.014 CrossRefGoogle Scholar
  19. 19.
    Vyas BY, Maheshwari RP, Das B (2013) Improved fault analysis technique for protection of Thyristor controlled series compensated transmission line. Int J Electr Power Energy Syst 55:321–330. doi: 10.1016/j.ijepes.2013.09.015 CrossRefGoogle Scholar
  20. 20.
    Nobakhti SM, Akhbari M (2014) A new algorithm for fault location in series compensated transmission lines with TCSC. Int J Electr Power Energy Syst 57:79–89. doi: 10.1016/j.ijepes.2013.11.052 CrossRefGoogle Scholar
  21. 21.
    Abur A, Magnago FH (2000) Use of time delays between modal components in wavelet based fault localization. Int J Electr Power Energy Syst 22(6):397–403. doi: 10.1016/S0142-0615(00)00010-7 CrossRefGoogle Scholar
  22. 22.
    Daneshpooy A, Gole AM (2001) Frequency Response of the Thyristor Controlled Series Capacitor. IEEE Trans Power Deliv 16:53–58. doi: 10.1109/61.905587 CrossRefGoogle Scholar
  23. 23.
    Oppenheim M., Poggi, JM (2001). Wavelet Toolbox Users Guide, The Math Work IncGoogle Scholar
  24. 24.
    Jiang S, Annakkage UD, Gole AM (2006) A Platform for Validation of FACTS Models. IEEE Trans Power Deliv 21:484–491. doi: 10.1109/TPWRD.2005.852301 CrossRefGoogle Scholar
  25. 25.
    Specht D (1990) Probabilistic neural networks. Neural Netw 1990(3):109–118. doi: 10.1016/0893-6080(90)90049-Q CrossRefGoogle Scholar
  26. 26.
    Gaing ZL (2004) Wavelet-based neural network for power disturbance recognition and classification. IEEE Trans Power Deliv 2004(19):1560–1568. doi: 10.1109/TPWRD.2004.835281 CrossRefGoogle Scholar
  27. 27.
    Huang N, Xu D, Liu X, Lin L (2012) Power quality disturbances classification based on S-transform and probabilistic neural network. Neurocomputing 98:12–23. doi: 10.1016/j.neucom.2011.06.041 CrossRefGoogle Scholar
  28. 28.
    Perera N, Rajapakse AD (2008) Fast isolation of faults in transmission systems using current transients. Electr Power Syst Res 78(9):1568–1578. doi: 10.1016/j.epsr.2008.01.018 CrossRefGoogle Scholar
  29. 29.
    García-Gracia M, Montañés A, El Halabi N, Comech MP (2012) High resistive zero-crossing instant faults detection and localization scheme based on wavelet analysis. Electr Power Syst Res 92:138–144. doi: 10.1016/j.epsr.2012.06.005 CrossRefGoogle Scholar
  30. 30.
    Swetapadma A, Yadav A (2015) Improved fault location algorithm for multi-location faults, transforming faults and shunt faults in thyristor controlled series capacitor compensated transmission line. IET Gener Transm Distrib 9(13):1597–1607. doi: 10.1049/iet-gtd.2014.0981 CrossRefGoogle Scholar

Copyright information

© The Natural Computing Applications Forum 2016

Authors and Affiliations

  1. 1.Instituto Tecnologico de MoreliaMoreliaMexico
  2. 2.Pontificia Universidad Católica Madre y Maestra PUCMMSanto DomingoDominican Republic

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