Sag Source Location and Type Recognition via Attention-based Independently Recurrent Neural Network-Reference-Cited by-同舟云学术

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Sag Source Location and Type Recognition via Attention-based Independently Recurrent Neural Network

Published:2021 Issue:5 Volume:9 Page:1018-1031
ISSN:2196-5625
Container-title:Journal of Modern Power Systems and Clean Energy
language:en
Short-container-title:

Author:

Deng Yaping,Liu Xinghua,Jia Rong,Huang Qi,Xiao Gaoxi,Wang Peng

Publisher

Journal of Modern Power Systems and Clean Energy

Subject

Energy Engineering and Power Technology,Renewable Energy, Sustainability and the Environment

Cited by 21 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Estimating the Health State of Lithium-Ion Batteries Using an Adaptive Gated Sequence Network and Hierarchical Feature Construction;Batteries;2024-08-03

2. Most influential feature form for supervised learning in voltage sag source localization;Engineering Applications of Artificial Intelligence;2024-07

3. An efficient YOLO v3-based method for the detection of transmission line defects;Frontiers in Energy Research;2023-11-21

4. Neural Network Based Fault Location in Power Distribution System;2023 IEEE PES Innovative Smart Grid Technologies - Asia (ISGT Asia);2023-11-21

5. Network-side voltage sag source location method based on limited monitoring point data;2023 3rd New Energy and Energy Storage System Control Summit Forum (NEESSC);2023-09-26

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