Fault Location of Distribution Network for Wavelet Packet Energy Moment of Dragonfly Algorithm

  • Jundong Fu
  • Jinglin Yue
  • Li Chen
  • Tianhang Leng
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 890)


The supply and distribution fault location method of wavelet packet energy gray-level moment wavelet neural network based on dragonfly algorithm (DA) is proposed. First, it makes convolutional wavelet energy gray-level moment for faulted electrical information, and then extracts the energy gray-level moment of fault features as the eigenvector of wavelet neural network for training, while the network parameters of wavelet neural network are globally optimized by DA. Finally, compared with the supply and distribution network fault location algorithm of support vector machine based on the DA and the pure wavelet neural network for supply and distribution network fault location algorithm, the experimental simulation shows that the proposed method has faster convergence speed. It also has the advantages of rapid descent speed, high precision, less iterations, rapid convergence, high locating accuracy for faults, and short locating time.


Fault location Convolutional wavelet packet energy gray-level moment Dragonfly algorithm Wavelet neural network 


  1. 1.
    Mirjalili, S.: Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Comput. Appl. 27(4), 1053–1073 (2016)CrossRefGoogle Scholar
  2. 2.
    Daely, P.T., Shin, S.Y.: Range based wireless node localization using Dragonfly Algorithm. In: Eighth International Conference on Ubiquitous and Future Networks. IEEE (2016)Google Scholar
  3. 3.
    He, Y., Tan, Y., Sun, Y.: Wavelet neural network approach for fault diagnosis of analogue circuits. IEE Proc. Circ. Dev. Syst. 151(4), 379–384 (2004)CrossRefGoogle Scholar
  4. 4.
    Peng, B.: Fault diagnosis of rotating machinery based on improved wavelet neural network. J. Vib. Meas. Diagn. 27(1), 32–35 (2007)Google Scholar
  5. 5.
    Lin, F.J., Shieh, H.J., Huang, P.K.: Adaptive wavelet neural network control with hysteresis estimation for piezo-positioning mechanism. IEEE Trans. Neural Netw. 17(2), 432–444 (2006)CrossRefGoogle Scholar
  6. 6.
    Dong, L., Xiao, D., Liang, Y., et al.: Rough set and fuzzy wavelet neural network integrated with least square weighted fusion algorithm based fault diagnosis research for power transformers. Electr. Power Syst. Res. 78(1), 129–136 (2008)CrossRefGoogle Scholar
  7. 7.
    Isermann, R.: Fault-Diagnosis Systems: An Introduction from Fault Detection to Fault Tolerance, vol. 28, no. 2, pp. 195–197(2006)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Jundong Fu
    • 1
  • Jinglin Yue
    • 1
  • Li Chen
    • 1
  • Tianhang Leng
    • 1
  1. 1.School of Electrical and Automation EngineeringEast China Jiaotong UniversityNanchangChina

Personalised recommendations