Insulating Fault Diagnosis of XLPE Power Cables Using Multi-parameter Based on Artificial Neural Networks

An online monitoring system of XLPE power cables was introduced in the research at first. It could detect the parameters, including partial discharge, dielectric loss, and central insulation resistance and sheathing resistance. The BP artificial neural networks were applied to diagnose the insulating status of XLPE cables using the 16 parameters. The adopted transfer functions in the neural networks were hyperbolic tangent function and S-type function. In order to reduce the training time, the Levenberg-Marquardt training method was used. The experimental results showed that the BP artificial neural networks could be applied in fault diagnosis of XLPE power cables using multi-parameter and when the number of nerve unit in the implied layer was fourteen, the output error was least.

Authors and Affiliations

1. State Key Laboratory of Electrical Insulation for Power Equipment, Xi’an Jiaotong University, Xi’an, Shaanxi, 710049, China

   Xiaolin Chen, Yonghong Cheng, Zhelei Zhu & Xiaojun Xie

2. Graduate School at Shenzhen, Tsinghua University, Shenzhen, Guangdong, 518055, China

   Bo Yue

Editors and Affiliations

1. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

   Jun Wang

2. The Key Laboratory of Optoelectric Technology & Systems, Ministry of Education, China

   Xiao-Feng Liao

3. Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, P.R. China

   Zhang Yi

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© 2005 Springer-Verlag Berlin Heidelberg

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