声发射技术和人工神经网络在局部放电分类中的应用

Conference Record of the the 2002 IEEE International Symposium on Electrical Insulation (Cat. No.02CH37316) Pub Date : 2002-04-07 DOI:10.1109/ELINSL.2002.995895

Y. Tian, Paul Lewin, A. E. Davies, S. Sutton, S. Swingler

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引用次数: 19

摘要

研究了基于声发射测量和反向传播人工神经网络的局部放电检测、信号分析和模式识别方法。测量信号处理采用三维模式和短时间傅里叶变换(SDFT)。研究表明，结合SDFT分量的BP神经网络对不同PD模式进行分类，总体效果很好。

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Application of acoustic emission techniques and artificial neural networks to partial discharge classification

Partial discharge (PD) detection, signal analysis and pattern identification, using acoustic emission measurements and the back-propagation (BP) artificial neural network (ANN) have been investigated. The measured signals were processed using three-dimensional patterns and short duration Fourier transforms (SDFT). Investigation indicates that using BP ANN with the SDFT components for classifying different PD patterns provides very good overall results.

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Conference Record of the the 2002 IEEE International Symposium on Electrical Insulation (Cat. No.02CH37316)

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