Accurate Identification of Transformer Faults From Dissolved Gas Data Using Recursive Feature Elimination Method-Reference-Cited by-同舟云学术

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Accurate Identification of Transformer Faults From Dissolved Gas Data Using Recursive Feature Elimination Method

Published:2023-02 Issue:1 Volume:30 Page:466-473
ISSN:1070-9878
Container-title:IEEE Transactions on Dielectrics and Electrical Insulation
language:
Short-container-title:IEEE Trans. Dielect. Electr. Insul.

Author:

Das Suchandan¹^ORCID,Paramane Ashish¹^ORCID,Chatterjee Soumya²^ORCID,Rao U. Mohan³^ORCID

Affiliation:

1. Electrical Engineering Department, National Institute of Technology, Silchar, India

2. Electrical Engineering Department, National Institute of Technology, Durgapur, India

3. Department of Applied Sciences, University of Quebec at Chicoutimi, Chicoutimi, Canada

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/94/10026623/09925237.pdf?arnumber=9925237

Reference25 articles.

1. Improving Reliability of Transformers based on DGA Analysis using Machine Learning Techniques

2. A comprehensive comparative study of DGA based transformer fault diagnosis using fuzzy logic and ANFIS models

3. Induction of Decision Trees to Diagnose Incipient Faults in Power Transformers

4. Hybrid feature selection–artificial intelligence–gravitational search algorithm technique for automated transformer fault determination based on dissolved gas analysis

5. Dissolved gas analysis of insulating oil for power transformer fault diagnosis with deep belief network

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1. Research on Transformer Condition Prediction Based on Gas Prediction and Fault Diagnosis;Energies;2024-08-16

2. Hybrid Algorithm for Dynamic Fault Prediction of HVDC Converter Transformer Using DGA Data;IEEE Transactions on Dielectrics and Electrical Insulation;2024-08

3. Research on Transformer Fault Diagnosis by WOA‐SVM Based on Feature Selection and Data Balancing;IEEJ Transactions on Electrical and Electronic Engineering;2024-07-15

4. A Review of Predictive Analytics Models in the Oil and Gas Industries;Sensors;2024-06-20

5. Imbalanced Industrial Load Identification Based on Optimized CatBoost with Entropy Features;Journal of Electrical Engineering & Technology;2024-06-11

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