A transformer condition recognition method based on dissolved gas analysis features selection and multiple models fusion-Reference-Cited by-同舟云学术

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A transformer condition recognition method based on dissolved gas analysis features selection and multiple models fusion

Published:2023-08 Issue: Volume:123 Page:106518
ISSN:0952-1976
Container-title:Engineering Applications of Artificial Intelligence
language:en
Short-container-title:Engineering Applications of Artificial Intelligence

Author:

Han Xiaohui^ORCID,Huang Song,Zhang Xu,Zhu Yingkuo,An Guoqing,Du Zhenbin

Funder

Hebei Province Science and Technology Support Program

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Artificial Intelligence,Control and Systems Engineering

Reference36 articles.

1. A new fuzzy logic approach for consistent interpretation of dissolved gas-in-oil analysis;Abu-Siada;IEEE Trans. Dielectr. Electr. Insul.,2013

2. IEEE guide for the interpretation of gases generated in oil-immersed transformer;Anon,2008

3. Mineral oil-impregnated electrical equipment in service- guide to the interpretation of dissolved and free gases analysis;Anon,2015

4. IEEE guide for the interpretation of gases generated in mineral oil-immersed transformers;Anon,2019

5. Estimation of conductivity at reduced time for sensing moisture content of oil-paper insulation;Chatterjee;IEEE Sens. J.,2020

Cited by 5 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. Research on Transformer Condition Prediction Based on Gas Prediction and Fault Diagnosis;Energies;2024-08-16

2. Application of back propagation neural network in complex diagnostics and forecasting loss of life of cellulose paper insulation in oil-immersed transformers;Scientific Reports;2024-03-13

3. Transformer Fault Diagnosis Method Based on TimesNet and Informer;Actuators;2024-02-14

4. Discernment of transformer oil stray gassing anomalies using machine learning classification techniques;Scientific Reports;2024-01-03

5. Transformer fault classification for diagnosis based on DGA and deep belief network;Energy Reports;2023-11

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