Details

Title

Detection and classification of short-circuit faults on a transmission line using current signal

Journal title

Bulletin of the Polish Academy of Sciences Technical Sciences

Yearbook

2021

Volume

69

Issue

4

Authors

Affiliation

Coban, Melih : Bolu Abant Izzet Baysal University, Bolu, Turkey ; Coban, Melih : Gazi University, Ankara, Turkey ; Tezcan, Suleyman S. : Gazi University, Ankara, Turkey

Keywords

transmission line ; fault detection ; fault classification ; support vector machine

Divisions of PAS

Nauki Techniczne

Coverage

e137630

Bibliography

  1.  M.M. Saha, J. Izykowski, and E. Rosolowski, Fault location on power networks. London: Springer, 2010.
  2.  M.B. Chatterjee and S. Debnath, “Cross correlation aided fuzzy based relaying scheme for fault classification in transmission lines,” Eng. Sci. Technol. Int J., vol. 23, no. 3, pp. 534–543, 2020, doi: 10.1016/j.jestch.2019.07.002.
  3.  A. Mukherjee, P.K. Kundu, and A. Das, “Classification and localization of transmission line faults using curve fitting technique with Principal component analysis features,” Electr. Eng., 2021, doi: 10.1007/s00202-021-01285-7.
  4.  R. Godse and S. Bhat, “Mathematical Morphology-Based Feature-Extraction Technique for Detection and Classification of Faults on Power Transmission Line,” IEEE Access, vol. 8, pp. 38459–38471, 2020, doi: 10.1109/access.2020.2975431.
  5.  Y. Liu, Y. Zhu, and K.Wu, “CNN-Based Fault Phase Identification Method of Double Circuit Transmission Lines,” Electr. Power Compon. Syst., vol. 48, no. 8, pp. 833–843, 2020, doi: 10.1080/15325008.2020.1821836.
  6.  M. Paul and S. Debnath, “Fault Detection and Classification Scheme for Transmission Lines Connecting Windfarm Using Single end Impedance,” IETE J. Res., pp. 1–13, 2021, doi: 10.1080/03772063.2021.1886601.
  7.  Y. Aslan and Y. E. Yağan, “Artificial neural-networkbased fault location for power distribution lines using the frequency spectra of fault data,” Electr. Eng., vol. 99, no. 1, pp. 301–311, 2016, doi: 10.1007/s00202-016-0428-8.
  8.  S. Ekici, “Support Vector Machines for classification and locating faults on transmission lines,” Appl. Soft Comput., vol. 12, no. 6,pp. 1650– 1658, 2012, doi: 10.1016/j.asoc.2012.02.011.
  9.  S.R. Samantaray, “A systematic fuzzy rule based approach for fault classification in transmission lines,” Appl. Soft Comput., vol. 13, no. 2, pp. 928–938, 2013, doi: 10.1016/j.asoc.2012.09.010.
  10.  S.R. Samantaray, P.K. Dash, and G. Panda, “Fault classification and location using HS-transform and radial basis function neural network,” Electr. Power Syst. Res., vol. 76, no. 9‒10, pp. 897–905, 2006, doi: 10.1016/j.epsr.2005.11.003.
  11.  A.A. Girgis and E.B. Makram, “Application of adaptive Kalman filtering in fault classification, distance protection, and fault location using microprocessors,” IEEE Trans. Power Syst., vol. 3, no. 1, pp. 301–309, 1988, doi: 10.1109/59.43215.
  12.  N. Ramesh Babu and B. Jagan Mohan, “Fault classification in power systems using EMD and SVM,” Ain Shams Eng. J., vol. 8, no. 2, pp. 103–111, 2017, doi: 10.1016/j.asej.2015.08.005.
  13.  F. Martin and J.A. Aguado, “Wavelet-based ann approach for transmission line protection,” IEEE Trans. Power Deliv., vol. 18, no. 4, pp. 1572–1574, 2003, doi: 10.1109/tpwrd.2003.817523.
  14.  O.A.S. Youssef, “Combined Fuzzy-Logic Wavelet-Based Fault Classification Technique for Power System Relaying,” IEEE Trans. Power Deliv., vol. 19, no. 2, pp. 582–589, 2004, doi: 10.1109/tpwrd.2004.826386.
  15.  A. Yadav and Y. Dash, “An Overview of Transmission Line Protection by Artificial Neural Network: Fault Detection, Fault Classification, Fault Location, and Fault Direction Discrimination,” Adv. Artif. Neural Syst., vol. 2014, pp. 1–20, 2014, doi: 10.1155/2014/230382.
  16.  M. Fikri and M.A.H. El-Sayed, “New algorithm for distance protection of high voltage transmission lines,” IEE Proc. C Gener. Transm. Distrib., vol. 135, no. 5, p. 436, 1988, doi: 10.1049/ip-c.1988.0056.
  17.  S. Mallat, “A Theory for Multiresolution Signal Decomposition: The Wavelet Representation,” Fundamental Papers in Wavelet Theory, 2009, pp. 494–513, doi: 10.1109/34.192463.
  18.  R. Salat and S. Osowski, “Accurate Fault Location in the Power Transmission Line Using Support Vector Machine Approach,” IEEE Trans. Power Syst., vol. 19, no. 2, pp. 979–986, 2004, doi: 10.1109/tpwrs.2004.825883.
  19.  P.K. Dash, S.R. Samantaray, and G. Panda, “Fault Classification and Section Identification of an Advanced Series-Compensated Transmission Line Using Support Vector Machine,” IEEE Trans. Power Deliv., vol. 22, no. 1, pp. 67–73, 2007, doi: 10.1109/tpwrd.2006. 876695.
  20.  V. Vapnik, “The Support Vector Method of Function Estimation,” Nonlinear Modeling, 1998, pp. 55–85, doi: 10.1007/978-1-4615-5703- 6_3.
  21.  Chih-Wei Hsu and Chih-Jen Lin, “A comparison of methods for multiclass support vector machines,” IEEE Trans. Neural Netw., vol. 13, no. 2, pp. 415–425, 2002, doi: 10.1109/72.991427.
  22.  S. Knerr, L. Personnaz, and G. Dreyfus, “Single-layer learning revisited: a stepwise procedure for building and training a neural network,” Neurocomputing, pp. 41–50, 1990, doi: 10.1007/978-3-642-76153-9_5.
  23.  J. Manit and P. Youngkong, Neighborhood components analysis in sEMG signal dimensionality reduction for gait phase pattern recognition. 7th Int. Conf. on Broadband Communications and Biomedical Applications, 2011, doi: 10.1109/IB2Com.2011.6217897.
  24.  M. Akdag and S. Rustemli, “Transmission line fault location: Simulation of real faults using wavelet transform based travelling wave methods,” Bitlis Eren Univ. J. Sci. Technol., vol. 9, no. 2, pp. 88–98, 2019, doi: 10.17678/beuscitech.653273.
  25.  N. Perera and A.D. Rajapakse, “Recognition of Fault Transients Using a Probabilistic Neural-Network Classifier,” IEEE Trans. Power Deliv., vol. 26, no. 1, pp. 410–419, 2011, doi: 10.1109/tpwrd.2010.2060214.
  26.  D. Gogolewski and W. Makiela, “Problems of Selecting the Wavelet Transform Parameters in the Aspect of Surface Texture Analysis,” TEH VJESN, vol. 28, no. 1, 2021, doi: 10.17559/tv-20190312141348.
  27.  J. Ypsilantis et al., “Adaptive, rule based fault diagnostician for power distribution networks,” IEE Proc. C Gener. Transm. Distrib., vol. 139, no. 6, p. 461, 1992, doi: 10.1049/ip-c.1992.0064.
  28.  F.B. Costa, “Fault-Induced Transient Detection Based on Real-Time Analysis of the Wavelet Coefficient Energy,” IEEE Trans. Power Deliv., vol. 29, no. 1, pp. 140–153, 2014, doi: 10.1109/tpwrd.2013.2278272.
  29.  P. Kapler, “An application of continuous wavelet transform and wavelet coherence for residential power consumer load profiles analysis,” Bull. Pol. Acad Sci. Tech. Sci., vol. 69, no. 1, 2021, doi: 10.24425/bpasts.2020.136216.
  30.  Y.Q. Chen, O. Fink, and G. Sansavini, “Combined Fault Location and Classification for Power Transmission Lines Fault Diagnosis With Integrated Feature Extraction,” IEEE Trans. Ind. Electron., vol. 65, no. 1, pp. 561–569, 2018, doi: 10.1109/TIE.2017.2721922.
  31.  G. Revati and B. Sunil, “Combined morphology and SVM-based fault feature extraction technique for detection and classification of transmission line faults,” Turk. J. Electr. Eng. Comput. Sci., vol. 28, no. 5, pp. 2768–2788, 2020, doi: 10.3906/elk-1912-7.
  32.  B.Y. Vyas, R.P. Maheshwari, and B. Das, “Versatile relaying algorithm for detection and classification of fault on transmission line,” Electr. Power Syst. Res., vol. 192, p. 106913, 2021, doi: 10.1016/j.epsr.2020.106913.

Date

15.06.2021

Type

Article

Identifier

DOI: 10.24425/bpasts.2021.137630

Source

Bulletin of the Polish Academy of Sciences: Technical Sciences; 2021; e137630
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