Journal of Applied Science and Engineering

Published by Tamkang University Press


Impact Factor



Yang Lei1, Fan Yang1, Jinrui Tang2This email address is being protected from spambots. You need JavaScript enabled to view it., Bo Ma2, Guoqing Zhang3, Yongjiang Han3, Yu Shen1, and Zhichun Yang1

1State Grid Hubei Electric Power Research Institute, 227 Xudong Road, 430077, Wuhan, China

2School of Automation, Wuhan University of Technology, 122 Luoshi Road, 430077, Wuhan, China

3Zhilian Xinneng Electric Power Technology Company, 6 Kejiyuan Road, 430223, Wuhan, China


Received: December 5, 2023
Accepted: March 14, 2024
Publication Date: May 9, 2024

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation: ||  

In power transmission networks, traditional double-ended traveling-wave-based fault-location methods have been widely utilized, demonstrating position errors of less than five hundred meters. However, when these traditional methods are directly applied to electrical distribution systems, the complex structures and parameters of distribution feeders necessitate the installation of numerous sensors to capture traveling waves. To minimize the total number of sensors required, it is advisable to employ both aerial-mode and zero-mode traveling waves for fault location, instead of relying solely on aerial-mode waves. In this paper, the distributed parameter-based line model is constructed and Karenbuaer transformation is used to achieve the mathematical model of modal traveling wave propagation speed, which is associated with various frequencies. And then the propagation speeds of zero-mode and aerial-mode fault-generated traveling waves are analyzed and compared with each other under overhead lines and underground cables in electrical distribution systems. Furthermore, the propagation speed is examined in main feeders and laterals with varying line radiuses and heights. The results indicate that zero-mode traveling waves can be utilized for fault location, but how to extract the real propagation speed under different fault distances would be the key factors to realize accurate modal traveling-wave-based fault location.

Keywords: Electrical distribution systems, fault location, traveling wave, propagation speed, line parameters.

  1. [1] L. K. Mortensen, H. R. Shaker, and C. T. Veje, (2022) “Relative fault vulnerability prediction for energy distribution networks" Applied Energy 322: 119449. DOI: 10.1016/j.apenergy.2022.119449.
  2. [2] C. Yang, S. Lin, and M. Guo, (2022) “Multi-Frequency bands based Pole-to-Ground fault detection method for MMC-Based radial DC distribution systems" International Journal of Electrical Power Energy Systems 141: 108250. DOI: 10.1016/j.ijepes.2022.108250.
  3. [3] A. Shammah, A. A. El-Ela, and A. M. Azmy, (2012) “Optimal location of remote terminal units in distribution systems using genetic algorithm" Electric Power Systems Research 89: 165–170. DOI: 10.1016/j.epsr.2012.03.007.
  4. [4] P. Stefanidou-Voziki, N. Sapountzoglou, B. Raison, and J. Dominguez-Garcia, (2022) “A review of fault location and classification methods in distribution grids" Electric Power Systems Research 209: 108031. DOI: 10.1016/j.epsr.2022.108031.
  5. [5] L. Zang, G. Zou, C. Zhou, M. Zheng, and T. Du, (2022) “Ad-axis based current differential protection scheme for an active distribution network" Protection and Control of Modern Power Systems 7(2): 1–11. DOI: 10.1186/s41601-022-00243-0.
  6. [6] E. Gord, R. Dashti, M. Najafi, and H. R. Shaker, (2019) “Real fault section estimation in electrical distribution networks based on the fault frequency component analysis" Energies 12(6): 1145. DOI: 10.3390/en12061145.
  7. [7] R. Rubeena, M. R. Dadash Zadeh, and T. P. S. Bains, (2014) “An Accurate Offline Phasor Estimation for Fault Location in Series-Compensated Lines" IEEE Transactions on Power Delivery 29(2): 876–883. DOI: 10.1109/TPWRD.2013.2283454.
  8. [8] R. Benato, G. Rinzo, and M. Poli, (2019) “Overcoming the limits of the charge transient fault location algorithm by the artificial neural network" Energies 12(4): 722. DOI: 10.3390/en12040722.
  9. [9] S.-y. He, A. Cozza, and Y.-z. Xie, (2020) “On the Spatial Resolution of Fault-Location Techniques Based on Full-Fault Transients" IEEE Transactions on Power Delivery 35(3): 1527–1540. DOI: 10.1109/TPWRD.2019.2949914.
  10. [10] K. Jia, T. Feng, Q. Zhao, C. Wang, and T. Bi, (2020) “High Frequency Transient Sparse Measurement-Based Fault Location for Complex DC Distribution Networks" IEEE Transactions on Smart Grid 11(1): 312–322. DOI: 10.1109/TSG.2019.2921301.
  11. [11] L. Xie, L. Luo, J. Ma, Y. Li, M. Zhang, X. Zeng, and Y. Cao, (2022) “A novel fault location method for hybrid lines based on traveling wave" International Journal of Electrical Power & Energy Systems 141: 108102. DOI: 10.1016/j.ijepes.2022.108102.
  12. [12] J. Ding, L. Li, Y. Zheng, C. Zhao, H. Chen, and X. Wang, (2017) “Distributed travelling-wave-based fault location without time synchronisation and wave velocity error" IET Generation, Transmission & Distribution 11(8): 2085–2093. DOI: 10.1049/iet-gtd.2016.1778.
  13. [13] C. Orozco-Henao, A. Suman Bretas, J. MarínQuintero, A. Herrera-Orozco, J. D. Pulgarín-Rivera, and J. C. Velez, (2018) “Adaptive impedance-based fault location algorithm for active distribution networks" Applied sciences 8(9): 1563. DOI: 10.3390/app8091563.
  14. [14] M. Daisy and R. Dashti, (2016) “Single phase fault location in electrical distribution feeder using hybrid method" Energy 103: 356–368. DOI: 10.1016/
  15. [15] X. Tian and H. Shu, (2021) “A new method of single terminal traveling wave location based on characteristic of superposition of forward traveling wave and backward traveling wave" International Journal of Electrical Power Energy Systems 133: 107072. DOI: 10.1016/j.ijepes.2021.107072.
  16. [16] W. Chen, D. Wang, D. Cheng, F. Qiao, X. Liu, and M. Hou, (2022) “Novel travelling wave fault location principle based on frequency modification algorithm" International Journal of Electrical Power Energy Systems 141: 108155. DOI: 10.1016/j.ijepes.2022.108155.
  17. [17] V. Gonzalez-Sanchez, V. Torres-García, and D. Guillen, (2021) “Fault location on transmission lines based on travelling waves using correlation and MODWT" Electric Power Systems Research 197: 107308. DOI: 10.1016/j.epsr.2021.107308.
  18. [18] A. Tanwani, A. D. Dominguez-Garcia, and D. Liberzon, (2011) “An Inversion-Based Approach to Fault Detection and Isolation in Switching Electrical Networks" IEEE Transactions on Control Systems Technology 19(5): 1059–1074. DOI: 10.1109/TCST.2010.2067214.
  19. [19] F. G. Y. Souhe, A. T. Boum, P. Ele, C. F. Mbey, V. J. F. Kakeu, et al., (2022) “Fault detection, classification and location in power distribution smart grid using smart meters data" Journal of Applied Science and Engineering 26(1): 23–34. DOI: 10.6180/jase.202301_26(1).0003.
  20. [20] J. B. Thomas, S. G. Chaudhari, S. K. V., and N. K. Verma, (2023) “CNN-Based Transformer Model for Fault Detection in Power System Networks" IEEE Transactions on Instrumentation and Measurement 72: 1– 10. DOI: 10.1109/TIM.2023.3238059.
  21. [21] W. Elmasry and M. Wadi, (2022) “Edla-efds: A novel ensemble deep learning approach for electrical fault detection systems" Electric Power Systems Research 207: 107834. DOI: 10.1016/j.epsr.2022.107834.
  22. [22] F. M. Shakiba, S. M. Azizi, M. Zhou, and A. Abusorrah, (2023) “Application of machine learning methods in fault detection and classification of power transmission lines: a survey" Artificial Intelligence Review 56(7): 5799–5836. DOI: 10.1007/s10462-022-10296-0.



69th percentile
Powered by  Scopus

SCImago Journal & Country Rank

Enter your name and email below to receive latest published articles in Journal of Applied Science and Engineering.