Feature engineering and feature selection for fault type classification from dissolved gas values in transformer oil
Author:
Affiliation:
1. The University of Edinburgh,School of Informatics,Edinburgh,United Kingdom
2. Sirindhorn International Institute of Technology, Thammasat University,School of ICT,Pathum Thani,Thailand
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9684498/9684574/09684595.pdf?arnumber=9684595
Reference20 articles.
1. A Fault Diagnosis Model of Power Transformers Based on Dissolved Gas Analysis Features Selection and Improved Krill Herd Algorithm Optimized Support Vector Machine
2. Feature selection in power transformer fault diagnosis based on dissolved gas analysis
3. Gas Chromatography
4. Gas Chromatography;wolstenholme;Analytical Techniques in Forensic Science”,2020
5. IEEE Guide for the Interpretation of Gases Generated in Mineral Oil-Immersed Transformers
Cited by 2 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. A Review of Predictive Analytics Models in the Oil and Gas Industries;Sensors;2024-06-20
2. Machine Learning Algorithms Fusion Based on DGA Data for Improving Fault Diagnosis of Electrical Power Transformer;The Scientific Bulletin of Electrical Engineering Faculty;2023-12-01
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