Transformer fault diagnosis method based on SMOTE and NGO-GBDT

Abstract In order to improve the accuracy of transformer fault diagnosis and improve the influence of unbalanced samples on the low accuracy of model identification caused by insufficient model training, this paper proposes a transformer fault diagnosis method based on SMOTE and NGO-GBDT. Firstly, t...

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Bibliographic Details
Published in:Scientific Reports
Main Authors: Li-zhong Wang, Jian-fei Chi, Ye-qiang Ding, Hai-yan Yao, Qiang Guo, Hai-qi Yang
Format: Article in Journal/Newspaper
Language:English
Published: Nature Portfolio 2024
Subjects:
Fault diagnosis
Transformers
Oversampling
LightGBM feature selection
GBDT
Northern goshawk optimization algorithm
Medicine
R
Science
Q
Northern Goshawk
Online Access:https://doi.org/10.1038/s41598-024-57509-w
https://doaj.org/article/38e66cb0728945558fa595dc4ef06e25
Description
Summary:Abstract In order to improve the accuracy of transformer fault diagnosis and improve the influence of unbalanced samples on the low accuracy of model identification caused by insufficient model training, this paper proposes a transformer fault diagnosis method based on SMOTE and NGO-GBDT. Firstly, the Synthetic Minority Over-sampling Technique (SMOTE) was used to expand the minority samples. Secondly, the non-coding ratio method was used to construct multi-dimensional feature parameters, and the Light Gradient Boosting Machine (LightGBM) feature optimization strategy was introduced to screen the optimal feature subset. Finally, Northern Goshawk Optimization (NGO) algorithm was used to optimize the parameters of Gradient Boosting Decision Tree (GBDT), and then the transformer fault diagnosis was realized. The results show that the proposed method can reduce the misjudgment of minority samples. Compared with other integrated models, the proposed method has high fault identification accuracy, low misjudgment rate and stable performance.

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