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Online fault recognition of electric power cable in coal mine based on the minimum risk neural network

Journal of Coal Science and Engineering (China) volume 14, pages 492–496 (2008)Cite this article

Abstract

Firstly, the concepts of the traveling wave entropy and the feature function of traveling wave entropy were defined. Then the statistic characters of the traveling wave entropy feature function, mean value and variance were analyzed after the zero-order component of the traveling wave of online cable was selected to serve as the observed object. Finally, the new recognition algorithm of minimum risk neural network was presented. The simulation experiments show that the recognitions of the early fault states can be completed correctly by using the proposed recognition algorithm. The classes of cable faults include in 1-phase ground faults, and the 2-phase short circuit faults or ground faults, and the 3-phase short circuit faults or ground faults, open circuit. The fault resistance range is 1×10−1∼1×109 Ω.

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Authors and Affiliations

  1. School of Electric and Control Engineering, Xi’an University of Science and Technology, Xi’an, 710054, China

    Mei Wang  (汪 梅)

  2. Department of Electrical and Electronic Engineering, Imperial College, London, SW7 2AZ, UK

    Mei Wang  (汪 梅) & Tania Stathaki

Authors
  1. Mei Wang  (汪 梅)
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  2. Tania Stathaki
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Corresponding author

Correspondence to Mei Wang  (汪 梅).

Additional information

Supported by the Science and Technology Foundation of Shaanxi Province in China (2003K06G19)

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Wang, M., Stathaki, T. Online fault recognition of electric power cable in coal mine based on the minimum risk neural network. J Coal Sci Eng China 14, 492–496 (2008). https://doi.org/10.1007/s12404-008-0106-1

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  • DOI: https://doi.org/10.1007/s12404-008-0106-1

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