Protection of transmission line and distribution system by using support vector machine: a Review

Author Affiliations

  • 1Department of Electrical Engineering, BIT, Durg, CG, India
  • 2Department of Electrical Engineering, BIT, Durg, CG, India

Res. J. Engineering Sci., Volume 6, Issue (7), Pages 28-32, July,26 (2017)

Abstract

In this article, an overview of the protection of transmission lines and distribution system is given with the help of a support vector machine. The errors of investigation and their causes have an essential basis for a secure and consistent power supply always. Rapid changes in the supply system due to disturbances, grid changes due to line trip, and break a large load or generating unit, force the rest to steer and solve new stable conditions. Necessary measures must be taken to protect the transmission and distribution system, such as error detection, classification and localization of errors. In the transmission and distribution system, fault classification mainly adopted well-developed by applying the use of algorithms of machine learning such as, for example, artificial neural networks, fuzzy logic and support vector machines.

References

  1. Maori A., Tripathy M. and Gupta H.O. (2014)., SVM based zonal setting of Mho relay for transmission line having TCSC., In Power India International Conference (PIICON), 2014 6th IEEE, IEEE, 1-5.
  2. Dash P.K., Samantaray S.R. and Panda G. (2007)., Fault classification and section identification of an advanced series-compensated transmission line using support vector machine., IEEE transactions on power delivery, 22(1), 67-73.
  3. Kumar Ravi B., Thukaram D. and Khincha H.P. (2010)., Comparison of multiclass SVM classification methods to use in a supportive system for distance relay coordination., IEEE Transactions on Power Delivery, 25(3), 1296-1305.
  4. Thukaram D. and Agrawal R. (2010)., Discrimination of Faulted Transmission Lines Using Multi Class Support Vector Machines., In16th National Power Systems Conference, 497-502.
  5. Seethalekshmi K., Singh S.N. and Srivastava S.C. (2012)., A classification approach using support vector machines to prevent distance relay mal operation under power swing and voltage instability., IEEE Transactions on Power Delivery, 27(3), 1124-1133.
  6. Jafarian P. and Sanaye-Pasand M. (2013)., High-frequency transients-based protection of multiterminal transmission lines using the SVM technique., IEEE Transactions on Power Delivery, 28(1), 188-196.
  7. Thirumala K., Maganuru S.P., Jain T. and Umarikar A. (2016)., Tunable-Q Wavelet Transform and Dual Multiclass SVM for Online Automatic Detection of Power Quality Disturbances., IEEE Transactions on Smart Grid.
  8. Singh M.R., Chopra T., Singh R. and Chopra T. (2015)., Fault Classification in Electric Power Transmission Lines using Support Vector Machine., International Journal, 1, 388-400.
  9. Khaled A., Mohamed M. and Nizam M.K. (2014)., Inayati. Voltage Problem area Classification using Support Vector Machine SVM., In International conference data, Civil and Mechanical Engineering (ICDMCME), Bali (Indonesia) , 214064, 4-5.
  10. Shahid N., Aleem S.A., Naqvi I.H. and Zaffar N. (2012)., Support vector machine based fault detection & classification in smart grids., In Globecom Workshops (GC Wkshps), 2012 IEEE, 1526-1531.
  11. Samantaray S.R. and Dash P.K. (2009)., High impedance fault detection in distribution feeders using extended kalman filter and support vector machine., International Transactions on Electrical Energy Systems, 20(3).
  12. Sun S. and Zhao H. (2013)., Fault diagnosis in railway track circuits using Support Vector Machines., In Machine Learning and Applications (ICMLA), 2013 12th International Conference on, 2, 345-350.
  13. Yang Y., Du Q. and Zhao J. (2010)., The application of sites selection based on AHP-SVM in 500KV substation., In Logistics Systems and Intelligent Management, 2010 International Conference on, 2, 1225-1229.
  14. Malathi V. and Marimuthu N.S. (2010)., Wavelet transform and support vector machine approach for fault location in power transmission line., International Journal of Electrical and Electronics Engineering, 4(4).
  15. Lin K.M. and Lin C.J. (2003)., A study on reduced support vector machines., IEEE transactions on Neural Networks, 14(6), 1449-1459.
  16. Smola A.J. and Schölkopf B. (2004)., A tutorial on support vector regression., Statistics and computing, 14(3), 199-222.
  17. Kwok J.Y. (1999)., Moderating the outputs of support vector machine classifiers., IEEE Transactions on Neural Networks, 10(5), 1018-1031.
  18. Hsu C.W., Chang C.C. and Lin C.J. (2003)., A practical guide to support vector classification., 1, 1-16.
  19. Campbell C. and Ying Y. (2011)., Learning with support vector machines., Synthesis lectures on artificial intelligence and machine learning, 5(1), 1-95.
  20. Weston J. (2009)., Support Vector Machine (and Statistical Learning Theory)., NFC, labs America, 4.