Abdelmoumene Hechifa, Abdelaziz LAKEHAL, Chouaib Labiod and Ridha Kelaiaia (2023) Improving The Fault Diagnosis Of Oil-Filled Transformers Based On Feature Selection Of Multiple Input Vectors. Journée d’étude sur l’intelligence artificielle en électromécanique et ses applications industrielles (JEIAM’23) , El Oued, Algeria
Scientific Publications
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Abstract
Ensuring the reliable and uninterrupted operation of power system networks relies on the early and
accurate detection of faults in oil-filled transformers. Dissolved gas analysis (DGA) is a widely employed technique for diagnosing transformer faults by analyzing the dissolved gases present in the insulating oil, which serve as indicators of electrical and thermal stresses.
accurate detection of faults in oil-filled transformers. Dissolved gas analysis (DGA) is a widely employed technique for diagnosing transformer faults by analyzing the dissolved gases present in the insulating oil, which serve as indicators of electrical and thermal stresses.
Information
Item Type | Conference |
---|---|
Divisions |
» Laboratory of Research on Electromechanical and Dependability » Faculty of Science and Technology |
ePrint ID | 4719 |
Date Deposited | 2023-12-15 |
Further Information | Google Scholar |
URI | https://univ-soukahras.dz/en/publication/article/4719 |
BibTex
@inproceedings{uniusa4719,
title={Improving The Fault Diagnosis Of Oil-Filled Transformers Based On Feature Selection Of Multiple Input Vectors},
author={Abdelmoumene Hechifa, Abdelaziz LAKEHAL, Chouaib Labiod and Ridha Kelaiaia},
year={2023},
booktitle={Journée d’étude sur l’intelligence artificielle en électromécanique et ses applications industrielles (JEIAM’23)}
}
title={Improving The Fault Diagnosis Of Oil-Filled Transformers Based On Feature Selection Of Multiple Input Vectors},
author={Abdelmoumene Hechifa, Abdelaziz LAKEHAL, Chouaib Labiod and Ridha Kelaiaia},
year={2023},
booktitle={Journée d’étude sur l’intelligence artificielle en électromécanique et ses applications industrielles (JEIAM’23)}
}