MRATNet: Learning Discriminative Features for Partial Discharge Pattern Recognition via Transformers-Reference-Cited by-同舟云学术

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MRATNet: Learning Discriminative Features for Partial Discharge Pattern Recognition via Transformers

Published:2024-08 Issue:4 Volume:31 Page:2198-2207
ISSN:1070-9878
Container-title:IEEE Transactions on Dielectrics and Electrical Insulation
language:
Short-container-title:IEEE Trans. Dielect. Electr. Insul.

Author:

Deng Yi¹,Zhu Kuihu²^ORCID,Liu Jiazheng²,Liu Hai³^ORCID

Affiliation:

1. School of Electronic and Electrical Engineering and the State Key Laboratory of New Textile Materials and Advanced Processing Technologies, Wuhan Textile University, Wuhan, China

2. School of Electronic and Electrical Engineering, Wuhan Textile University, Wuhan, China

3. National Engineering Research Center for E-Learning, Central China Normal University, Wuhan, China

Funder

Program for Excellent Middle-Aged and Young Scientist Science and Technology Innovative Research Team in the Higher Education Institutions of Hubei

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/94/10623348/10528343.pdf?arnumber=10528343

Reference43 articles.

1. Industrial Applications of Cable Diagnostics and Monitoring Cables via Time–Frequency Domain Reflectometry

2. Cable Thermal Risk Estimation for Overplanted Wind Farms

3. Single-Ended Fault Location for Hybrid Feeders Based on Characteristic Distribution of Traveling Wave Along a Line

4. Improved information entropy weighted vague support vector machine method for transformer fault diagnosis

5. Identification of Partial Discharge Defects Based on Deep Learning Method

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