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Journal of Vibration Testing and System Dynamics

C. Steve Suh (editor), Pawel Olejnik (editor),

Xianguo Tuo (editor)

Pawel Olejnik (editor)

Lodz University of Technology, Poland

Email: pawel.olejnik@p.lodz.pl

C. Steve Suh (editor)

Texas A&M University, USA

Email: ssuh@tamu.edu

Xiangguo Tuo (editor)

Sichuan University of Science and Engineering, China

Email: tuoxianguo@suse.edu.cn


A New Method of Fault Phase Selection for Transmission Line Based on Composite Feature of Fault Current and Support Vector Machine

Journal of Vibration Testing and System Dynamics 6(2) (2022) 215--234 | DOI:10.5890/JVTSD.2022.06.004

Jie Yang$^{1}$, Hao Wu$^{1,2 }$, Sheng Wang$^{1}$, Xing-xing Dong$^{1}$

$^1$ Automation and Information Engineering, Sichuan University of Science and Engineering, Zigong 643000, China

$^2$ Sichuan Key Laboratory of Artificial Intelligence, Zigong, Sichuan 643000, China

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Abstract

Aiming at the problem of fault phase selection for high-voltage transmission lines, a new method for identifying fault types of transmission lines based on the composite characteristics of fault current combined with support vector machines (SVM) is proposed. First, perform phase-to-mode conversion on the three-phase currents fault component extracted by the measurement unit, and then obtain the Euclidean distance, cosine similarity and Pearson correlation coefficient between the two modulus currents, and finally combine the calculated results in order. Characteristic sample vector to characterize the type of transmission line fault. Then use the powerful pattern recognition capability of the support vector machine to identify the fault type. The simulation results show that the proposed algorithm can accurately identify specific fault types of transmission lines under various working conditions.

References

  1. [1]  Gaur, V.K., Bhalia, B.R., and Mladen, K. (2021), Novel Fault Distance Estimation Method for Three-Terminal Transmission Line, IEEE Transactions on Power Delivery, 36(1), 406-417.
  2. [2]  Mahdim, M., Vahidi, B.H., and Hossein, S. (2019), Accurate fault location and faulted section determination based on deep learning for a parallel-compensated three-terminal transmission line, IET Generation Transmission and Distribution, 13(13), 2770-2778.
  3. [3]  Fan, X.Q, and Zhu, Y.L. (2011), Fault phase selection of high voltage transmission line based on high order multi-resolution singular entropy, Electric Power Automation Equipment, 31(04), 50-54.
  4. [4]  Lin, S., He, Z.Y., Chen, S., et al. (2011), A new method of fault line selection and phase selection based on zero sequence voltage regulation, Journal of Southwest Jiaotong University, 46(04), 611-619.
  5. [5]  Shaik, A.G., Yadav, S.K., Prashanth, P., et el (2014), Transmission line protection scheme using Wavelet based alienation coefficients, IEEE International Conference on Power and Energy (PECon), 32-36.
  6. [6]  Mahdi, M., Vahidi, B., and Hossein, S. (2018), Fault location on a series-compensated three-terminal transmission line using deep neural networks, IET science measurement $\&$ technology, 12(6), 746-754.
  7. [7]  Lin, X.N., Liu, P., Yang, C.M., et al (2002), Fault sequence component phase selector based on correlation analysis, Proceedings of the CSEE, 2002(05), 17-22.
  8. [8]  Huang, S.F., Luo, L., and Cao, K. (2014), A novel method of ground fault phase selection in weak-infeed side, IEEE Transactions on Power Delivery, 29(5), 2215-2222.
  9. [9]  Xu, Q.Q., Suo, N.J.L., Song, G.B., et al (2003), A phase selector for high voltage line protection with current fault component, automation of electric power systems, 2003(07), 50-54.
  10. [10]  Lu, W.J., Lin, X.N., Huang, X.B., et al. (2007), A new type of sudden variable phase selector adapting to the change of power system operation mode, Proceedings of the CSEE, 2007(28), 53-58.
  11. [11]  Wu, L., Gu, B., and Tan, J.C.(2008), A phase selector for high voltage line protection based on power increment, Transactions of China Electrotechnical Society, 2008(06), 125-129.
  12. [12]  Li, X., Gong, Q.W., and Jia, J.J. (2011), The principle of super phase selection based on wavelet transform is studied, Power System Protection and Control, 39(15), 57-63.
  13. [13]  Wang, A.J., Li, H., and Zhang, X.T. (2013), A fault phase selection method for EHV transmission lines based on Wavelet Transform, Power System Protection and Control, 41(12), 92-97.
  14. [14]  Cui, C.Q., Wang, Z.S., Yang, D.S., et al (2017), Fault location and phase selection of transmission line based on Wavelet Transform, Control Engineering Of China, 24(S1), 85-91.
  15. [15]  Jing, J.A., Fan, P.L., Chen, C.S., et al (2003), A fault detection and faulted-phase selection approach for transmission lines with Haar wavelet transform, IEEE PES Transmission and Distribution Conference and Exposition, 1(1), 285-289.
  16. [16]  Shaik, A.G., Yadav, S.K., Prashanth, P., et al (2003), Transmission line protection scheme using Wavelet based alienation coefficients, IEEE International Conference on Power and Energy (PECon), 2014, 32-36.
  17. [17]  Liu, D., Zou, G.B., Wang, X., et al (2015), A fast fault phase selection method for transmission lines based on S-transform, Power System Technology, 39(12), 3603-3608.
  18. [18]  Wu, H., Guo, H., and Cai, L.(2013), Fault phase selection based on fault component energy coefficient and PNN, High Voltage Apparatus, 49(08), 35-43.
  19. [19]  Yang, J., Wu, H., Hu, X.T., et al (2020), Fault identification method of T-connected line based on multi-scale traveling wave power, Proceedings of the CSU-EPSA, 1-11.
  20. [20]  Bu, C.X., Zhang, Y.H., Jiang, Z.Q., et al (2010), Study on phase selection of transient protection for EHV transmission lines, Power System Protection and Control, 38(16), ,30-34.
  21. [21]  Chen, L., Pan, Y., and Chen, Y.X. (2004), Efficient Parallel Algorithms for Euclidean Distance Transform, The Computer Journal, 47(6), 694-700.
  22. [22]  Dong, X.X., Peng, Q., Wu, H., et al (2019), New principle for busbar protection based on the Euclidean distance algorithm, PLOS ONE, 14(7), e0219320.
  23. [23]  Hong, C., Fu, Y.Z., Guo, M.F., et al (2019), Distribution network fault identification method based on improved multi class support vector machine, Journal of Electronic Measurement and Instrument, 33(01), 7-15.
  24. [24]  Wang, D.M., Lu, C.H., Jiang, W.W., et al (2015), Research on SVM multi classification method of decision tree based on particle swarm optimization, Journal of Electronic Measurement and Instrument, 29(04), 611-615.
  25. [25]  Wang, Z.H., Fang, C., He, F.P., et al (2014), Lithology spectral classification based on decision tree multi classification support vector machine, ACTA Scientiarum Naturalium Universitatis Sunyatseni, 53(06), 93-97+105