Shape Preserving Incremental Learning for Power Systems Fault Detection-Reference-Cited by-同舟云学术

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Shape Preserving Incremental Learning for Power Systems Fault Detection

Published:2019-01 Issue:1 Volume:3 Page:85-90
ISSN:2475-1456
Container-title:IEEE Control Systems Letters
language:
Short-container-title:IEEE Control Syst. Lett.

Author:

Cordova Jose^ORCID,Soto Carlos,Gilanifar Mostafa,Zhou Yuxun,Srivastava Anuj,Arghandeh Reza^ORCID

Funder

European Union’s Horizon 2020 (ERIGrid) through the Trans-National Access Programme

U.S. DOE Grant RADIANCE GMLC

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Control and Optimization,Control and Systems Engineering

Link

https://ieeexplore.ieee.org/ielaam/7782633/8410052/8401489-aam.pdf

Cited by 10 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. Long-short term memory networks aided fault detection of power facilities;Intelligent Systems with Applications;2024-06

2. Machine learning techniques applied on a nine-phase transmission line for faults classification and location;Energy Reports;2022-04

3. A Machine Learning Framework for Event Identification via Modal Analysis of PMU Data;IEEE Transactions on Power Systems;2022

4. Machine learning applications in power system fault diagnosis: Research advancements and perspectives;Engineering Applications of Artificial Intelligence;2021-11

5. Integrated Cyber and Physical Anomaly Location and Classification in Power Distribution Systems;IEEE Transactions on Industrial Informatics;2021-10

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