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An application of a discrete wavelet transform and a back-propagation neural network algorithm for fault diagnosis on single-circuit transmission line

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  • A. Ngaopitakkul
  • S. Bunjongjit

Abstract

This article proposes an application of the discrete wavelet transform (DWT) and back-propagation neural networks (BPNN) for fault diagnosis on single-circuit transmission line. ATP/EMTP is used to simulate fault signals. The mother wavelet daubechies4 (db4) is used to decompose the high-frequency component of these signals. In addition, characteristics of the fault current at various fault inception angles, fault locations and faulty phases are detailed. The DWT is employed in extracting the high frequency component contained in the fault currents, and the coefficients of the first scale from the DWT that can detect fault are investigated, and the decision algorithm is constructed based on the BPNN. The results show that the proposed technique provides satisfactory results.

Suggested Citation

  • A. Ngaopitakkul & S. Bunjongjit, 2013. "An application of a discrete wavelet transform and a back-propagation neural network algorithm for fault diagnosis on single-circuit transmission line," International Journal of Systems Science, Taylor & Francis Journals, vol. 44(9), pages 1745-1761.
    • Handle: RePEc:taf:tsysxx:v:44:y:2013:i:9:p:1745-1761
    DOI: 10.1080/00207721.2012.670290
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    Cited by:

    1. Junxun Chen & Longsheng Cheng & Hui Yu & Shaolin Hu, 2018. "Rolling bearing fault diagnosis and health assessment using EEMD and the adjustment Mahalanobis–Taguchi system," International Journal of Systems Science, Taylor & Francis Journals, vol. 49(1), pages 147-159, January.
    2. Yumin Hsueh & Veeresha Ramesha Ittangihala & Wei-Bin Wu & Hong-Chan Chang & Cheng-Chien Kuo, 2019. "Condition Monitor System for Rotation Machine by CNN with Recurrence Plot," Energies, MDPI, vol. 12(17), pages 1-13, August.

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