Partial Discharge Recognition System for Current Transformer Using Neural Network and 2-D Wavelet Transform

勤益科大機構典藏 > 電資學院 > 電機工程系(所) > 【電機工程系所】期刊論文 > Item 987654321/7063

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題名:	Partial Discharge Recognition System for Current Transformer Using Neural Network and 2-D Wavelet Transform
作者:	郭應標
貢獻者:	電機工程(學)系
日期:	2012-03
上傳時間:	2018-01-07 11:06:48 (UTC+8)
摘要:	In this paper, a neural network and two-dimensional (2D) wavelet transform are applied to recognize partial discharge (PD) patterns on current transformers (CTs). To avoid the discrepancy between simulated results and real experimental data, we adopted seven cast-resin CTs that were purposely fabricated with various insulation defects as the PD patterns collected samples to actually emulate the various defects incurred often during their production. All measurements are taken in a shielded lab; the commercial TE571 PD detector is adopted to measure PD patterns to ensure the reliability of the PD signals. Next, we extract the patterns' features via a 2D wavelet transform and use the features as the training set of a backpropagation neural network (BNN) to construct the recognition system for CTs' PD patterns. Finally, we add random noises to the measured PD signals to emulate the field diagnosis under a high-noise environment. The study results indicate that, under a simulated noise magnitude of 30 pC, the recognition rate of the proposed system still can reach around 80%, signifying a great potential in applying the proposed recognition system in field measurements in the future. © 2011 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.
關聯:	IEEJ Transactions on Electrical and Electronic Engineering
顯示於類別:	[電機工程系(所)] 【電機工程系所】期刊論文

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