Fault Diagnosis of Transformer Windings Based on Decision Tree and Fully Connected Neural Network

While frequency response analysis (FRA) is a well matured technique widely used by current industry practice to detect the mechanical integrity of power transformers, interpretation of FRA signatures is still challenging, regardless of the research efforts in this area. This paper presents a method...

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Bibliographic Details
Published in:Energies (Basel) 2021-03, Vol.14 (6), p.1531
Main Authors: Li, ZhenHua, Zhang, Yujie, Abu-Siada, Ahmed, Chen, Xingxin, Li, Zhenxing, Xu, Yanchun, Zhang, Lei, Tong, Yue
Format: Article
Language:English
Subjects:
decision tree
frequency response analysis
fully connected neural network
image processing
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Summary:While frequency response analysis (FRA) is a well matured technique widely used by current industry practice to detect the mechanical integrity of power transformers, interpretation of FRA signatures is still challenging, regardless of the research efforts in this area. This paper presents a method for reliable quantitative and qualitative analysis to the transformer FRA signatures based on a decision tree classification model and a fully connected neural network. Several levels of different six fault types are obtained using a lumped parameter-based transformer model. Results show that the proposed model performs well in the training and the validation stages, and is of good generalization ability.
ISSN:1996-1073
1996-1073
DOI:10.3390/en14061531