Improved intelligent methods for power transformer fault diagnosis based on tree ensemble learning and multiple feature vector analysis-Reference-Cited by-同舟云学术

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Improved intelligent methods for power transformer fault diagnosis based on tree ensemble learning and multiple feature vector analysis

Published:2023-11-03 Issue: Volume: Page:
ISSN:0948-7921
Container-title:Electrical Engineering
language:en
Short-container-title:Electr Eng

Author:

Hechifa Abdelmoumene,Lakehal Abdelaziz,Nanfak Arnaud,Saidi Lotfi,Labiod Chouaib,Kelaiaia Ridha,Ghoneim Sherif S. M.

Publisher

Springer Science and Business Media LLC

Subject

Applied Mathematics,Electrical and Electronic Engineering

Link

https://link.springer.com/content/pdf/10.1007/s00202-023-02084-y.pdf

Reference73 articles.

1. Liu Y, Song B, Wang L, Gao J, Xu R (2020) Power transformer fault diagnosis based on dissolved gas analysis by correlation coefficient-DBSCAN. Appl Sci. https://doi.org/10.3390/app10134440

2. Djaballah S, Meftah K, Khelil K, Sayadi M (2023) Deep transfer learning for bearing fault diagnosis using CWT time-frequency images and convolutional neural networks. J Fail Anal Prev. https://doi.org/10.1007/s11668-023-01645-4

3. Zhang D et al (2022) A bi-level machine learning method for fault diagnosis of oil-immersed transformers with feature explainability. Int J Electr Power Energy Syst 134:107356. https://doi.org/10.1016/j.ijepes.2021.107356

4. Ekojono RA, Prasojo ME, Apriyani RAN (2022) Investigation on machine learning algorithms to support transformer dissolved gas analysis fault identification. Electr Eng 104(5):3037–3047. https://doi.org/10.1007/s00202-022-01532-5

5. Han X, Ma S, Shi Z, An G, Du Z, Zhao C (2022) A novel power transformer fault diagnosis model based on Harris-Hawks-optimization algorithm optimized kernel extreme learning machine. J Electr Eng Technol 17(3):1993–2001. https://doi.org/10.1007/s42835-022-01000-x

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