Power Cable Fault Diagnosis Based on Wavelet Analysis and Neural Network

  • Minghang JiaoEmail author
  • Yang Gao
  • Xuemin Leng
  • Yangqun Ou
  • Lin Zhang
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 921)


With the widespread use of cables, the problem of power system fault diagnosis is becoming more and more serious. As we all know, the sudden blackout caused by cable fault will bring serious threat to the life and property safety of users, and even cause adverse social impact. Avoiding losses caused by cable failures is popular. To diagnose the cable faults is vital to guarantee the safe and steady operation of power transmission line. The combination of wavelet analysis and neural network is adopted as the fault diagnosis method to realize the accurate identification. Wavelet packet decomposition is used for feature extraction of cable fault signals which are input vectors after normalization processing. Radial basis function (RBF) network structure is built and relevant practice and test of cable fault diagnosis are conducted select 8 sets of samples for testing. By selecting different failover resistance values, the target output of the first four groups is 0.9, and the actual output is also 0.9; the target output of the last four groups is 0.1, the actual output is also 0.1, and the target output and actual output are The error between the two is basically zero, which also indicates that the RBF network has good fault discrimination. According to the test result, it shows that this method can be effectively achieved in cable faults diagnosis.


Cable fault diagnosis Wavelet analysis Neural network 


  1. 1.
    Bao, Y.Sh.: Partial discharge of power cable on-line monitoring and fault diagnosis. Beijing Jiaotong University (2012). (in Chinese)Google Scholar
  2. 2.
    Yuan, Y.L., Zhou, H., Dong, J.: On-line monitoring and fault diagnosis technology of high voltage power cable sheath current (in Chinese)Google Scholar
  3. 3.
    Zhai, L., Wen, Y.K.: Power cable fault diagnosis and analysis. In: Technology and Innovation, vol. 22, pp. 114–116 (2015). (in Chinese)Google Scholar
  4. 4.
    Liu, H.: Cable fault diagnosis theory and key technology research. Huazhong University of Science and Technology (2012). (in Chinese)Google Scholar
  5. 5.
    Zhang, G., Ye, J.H.: Intelligent fault diagnosis and management system for power cable. Power Syst. Telecommun. 02, 56–60 (2011). (in Chinese)Google Scholar
  6. 6.
    Gao, Q.S., Yang, J.: Review of fault diagnosis of power cables. Guizhou Electric Power Technol. 05, 54–58 (2016). (in Chinese)Google Scholar
  7. 7.
    Zhang, Z.Ch.: Partial discharge monitoring of power cables and insulation fault diagnosis. The Hubei University of Technology (2013). (in Chinese)Google Scholar
  8. 8.
    Fu, J.P.: Underground pipe network on-line monitoring and fault diagnosis system. University of Electronic Science and Technology (2013). (in Chinese)Google Scholar
  9. 9.
    Zhu, G.M.: Research on fault location method of power cable-based on wavelet neural network. Guangdong Electric Power (2013). (in Chinese)Google Scholar
  10. 10.
    Zhan, L.H.: Research on calculation of power cable core temperature based on BP neural network. Technology Information (2016). (in Chinese)Google Scholar
  11. 11.
    Yang, X.H.: Pattern recognition of partial discharge of XLPE power cable-based on BP artificial neural network. High Voltage Electrical Appliance (2013). (in Chinese)Google Scholar
  12. 12.
    Wang, Y.X.: Simulation analysis of partial discharge circuit model of power cable-based on capacitance sensor technology. Wire and Cable (2017). (in Chinese)Google Scholar
  13. 13.
    Press, W.H., Teukolsky, S.A., Vetterling, W.T., Flannery, B.P.: Section 3.7.1. radial basis function interpolation. Numerical Recipes: The Art of Scientific Computing, 3rd edn. Cambridge University Press, New York (2007)Google Scholar
  14. 14.
    Bishop, C.M.: Neural networks for pattern recognition. Clarendon Press, Oxford (1995)zbMATHGoogle Scholar
  15. 15.
    Graps, A.: An introduction to wavelets. J. IEEE Comput. Sci. Eng. 2(2), 50–61 (1995)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Minghang Jiao
    • 1
    Email author
  • Yang Gao
    • 2
  • Xuemin Leng
    • 2
  • Yangqun Ou
    • 1
  • Lin Zhang
    • 1
  1. 1.State Grid Liaoyang Electric Power Supply Co., Ltd.ShenyangChina
  2. 2.Shenyang Institute of EngineeringShenyangChina

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