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	A Sequence-to-Sequence Deep Learning Architecture Based on Bidirectional GRU for Type Recognition and Time Location of Combined Power Quality Disturbance
	Deng, Yaping; Wang, Lu; Jia, Hao; Tong, Xiangqian; Li, Feng
	2019
卷号	15页码:4481-4493
关键词	Bidirectional gated recurrent unit (Bi-GRU) deep learning power quality disturbance (PQD) sequence-to-sequence model time location type recognition
ISSN号	1551-3203
DOI	10.1109/TII.2019.2895054
URL标识	查看原文
WOS记录号	WOS:000480360800009
内容类型	期刊论文
URI标识	http://www.corc.org.cn/handle/1471x/4969655
专题	西安理工大学
推荐引用方式 GB/T 7714	Deng, Yaping,Wang, Lu,Jia, Hao,et al. A Sequence-to-Sequence Deep Learning Architecture Based on Bidirectional GRU for Type Recognition and Time Location of Combined Power Quality Disturbance[J],2019,15:4481-4493.
APA	Deng, Yaping,Wang, Lu,Jia, Hao,Tong, Xiangqian,&Li, Feng.(2019).A Sequence-to-Sequence Deep Learning Architecture Based on Bidirectional GRU for Type Recognition and Time Location of Combined Power Quality Disturbance.,15,4481-4493.
MLA	Deng, Yaping,et al."A Sequence-to-Sequence Deep Learning Architecture Based on Bidirectional GRU for Type Recognition and Time Location of Combined Power Quality Disturbance".15(2019):4481-4493.

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