A novel method for power transformer fault diagnosis considering imbalanced data samples

A novel method for power transformer fault diagnosis considering imbalanced data samples

IntroductionMachine learning-based power transformer fault diagnosis methods often grapple with the challenge of imbalanced fault case distributions across different categories, potentially degrading diagnostic accuracy. To address this issue and enhance the accuracy and operational efficiency of po...

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Bibliographic Details
Main Authors: Jun Chen, Yong Wang, Lingming Kong, Yilong Chen, Mianzhi Chen, Qian Cai, Gehao Sheng
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-01-01
Series:Frontiers in Energy Research
Subjects:
fault diagnosis
transformer
k-nearest neighbor learning
machine learning
imbalanced samples
Online Access:https://www.frontiersin.org/articles/10.3389/fenrg.2024.1500548/full
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