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	Assessment of PD severity in gas-insulated switchgear with an SSAE
	Tang, Ju; Jin, Miao; Zeng, Fuping; Zhang, Xiaoxing; Huang, Rui
刊名	IET SCIENCE MEASUREMENT & TECHNOLOGY
	2017
卷号	11期号:4
关键词	partial discharge measurement gas insulated switchgear statistical analysis neural nets learning (artificial intelligence) encoding feature extraction computerised instrumentation PD severity assessment gas-insulated switchgear SSAE partial discharge severity assessment discharge time discharge amplitude deep-learning neural network model stacked sparse autoencoder feature extraction soft-max classifier unsupervised greedy layer-wise pre-training method supervised fine-tuning method support vector machine algorithm
ISSN号	1751-8822
DOI	10.1049/iet-smt.2016.0326
URL标识	查看原文
收录类别	SCIE ; EI
语种	英语
内容类型	期刊论文
URI标识	http://www.corc.org.cn/handle/1471x/3890971
专题	武汉大学
推荐引用方式 GB/T 7714	Tang, Ju,Jin, Miao,Zeng, Fuping,et al. Assessment of PD severity in gas-insulated switchgear with an SSAE[J]. IET SCIENCE MEASUREMENT & TECHNOLOGY,2017,11(4).
APA	Tang, Ju,Jin, Miao,Zeng, Fuping,Zhang, Xiaoxing,&Huang, Rui.(2017).Assessment of PD severity in gas-insulated switchgear with an SSAE.IET SCIENCE MEASUREMENT & TECHNOLOGY,11(4).
MLA	Tang, Ju,et al."Assessment of PD severity in gas-insulated switchgear with an SSAE".IET SCIENCE MEASUREMENT & TECHNOLOGY 11.4(2017).

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