Feature selection for effective health index diagnoses of power transformers
Author | Benhmed, Kamel |
Author | Mooman, Abdelniser |
Author | Younes, Abdunnaser |
Author | Shaban, Khaled |
Author | El-Hag, Ayman |
Available date | 2022-12-21T10:01:47Z |
Publication Date | 2018 |
Publication Name | IEEE Transactions on Power Delivery |
Resource | Scopus |
Abstract | This letter investigates an approach based on feature selection and classification techniques to reduce assessment complexities of power transformers. This approach decreases the number of features by extracting the most influential ones when determining the transformers health index (HI). Several filters and wrapper-based feature selection methods are investigated. The effectiveness of the selected features is validated through performance evaluations of various classification models. The experimental results demonstrate that water content, acidity, breakdown voltage, and FFA (Furan), are the most influential testing parameters in determining the transformer HI. 1986-2012 IEEE. |
Language | en |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Subject | Artificial intelligence condition monitoring transformer |
Type | Article |
Pagination | 3223-3226 |
Issue Number | 6 |
Volume Number | 33 |
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Computer Science & Engineering [2284 items ]