Fault Location of Cabled Seafloor Observation Network Based on Fault Feature and Fault Distance Relation Mining Using Deep Learning
Author:
Affiliation:
1. University of Chinese Academy of Sciences,Beijing,China
2. The Institute of Acoustics of the Chinese Academy of Sciences,Beijing,China
3. Hainan Library Acoustics, Chinese Academy of Sciences,Haikou,Hainan,China
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10105556/10105661/10105701.pdf?arnumber=10105701
Reference11 articles.
1. Fault location method for seafloor observation network based on multi -terminal travelling wave time difference;zeng;Power System Technology,2019
2. Research on Wide Area Travelling Wave Fault Location Method Based on Distributed Travelling Wave Detection;deng;Power System Technology,2017
3. PCA/LSTM-Based Submarine Cable Fault Location Approach for Seafloor Observatory Network Power Systems;geng;Journal of Ocean Technology,2020
4. Current development review of submarine cable detection methods;ji;Southern Power System Technology,2021
5. Diagnosis and location of faults in submarine power cables
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