Imbalanced Sample Learning Method for Transformer Fault Diagnosis Based on GHM Loss and Stacking Frames-Reference-Cited by-同舟云学术

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Imbalanced Sample Learning Method for Transformer Fault Diagnosis Based on GHM Loss and Stacking Frames

Published:2024 Issue: Volume:73 Page:1-13
ISSN:0018-9456
Container-title:IEEE Transactions on Instrumentation and Measurement
language:
Short-container-title:IEEE Trans. Instrum. Meas.

Author:

Lantao Jing¹^ORCID,Ye Zhang¹^ORCID,Bin Zhang²^ORCID,Guangyan Sun¹^ORCID,Linqiao Wu¹^ORCID,Meiling Bao¹^ORCID,Liang Wang¹^ORCID

Affiliation:

1. Shenyang Institute of Engineering, Shenyang, China

2. Electric Power Research Institute of State Grid Liaoning Electric Power Company, Shenyang, China

Funder

Natural Science Foundation of Liaoning Provincial Department of Education of China

Basic Research Project of Liaoning Provincial Department of Education of China

Innovation Capability Enhancement Joint Fund

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/19/10367905/10555338.pdf?arnumber=10555338

Reference44 articles.

1. Hierarchical Federated Learning for Power Transformer Fault Diagnosis

2. You can get smaller: A lightweight self-activation convolution unit modified by transformer for fault diagnosis

3. Reliable IoT Paradigm With Ensemble Machine Learning for Faults Diagnosis of Power Transformers Considering Adversarial Attacks

4. Research on hybrid feature selection method of power transformer based on fuzzy information entropy

5. A New Method for Transformer Fault Prediction Based on Multifeature Enhancement and Refined Long Short-Term Memory

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