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

ZhenHua Li, Yujie Zhang, Ahmed Abu-Siada, Xingxin Chen, Zhenxing Li, Yanchun Xu, Lei Zhang and Yue Tong
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ZhenHua Li: College of Electrical Engineering & New Energy, China Three Gorges University, Yichang 443002, China
Yujie Zhang: College of Electrical Engineering & New Energy, China Three Gorges University, Yichang 443002, China
Ahmed Abu-Siada: Department of Electrical and Computer Engineering, Curtin University, Perth 6000, Australia
Xingxin Chen: College of Electrical Engineering & New Energy, China Three Gorges University, Yichang 443002, China
Zhenxing Li: College of Electrical Engineering & New Energy, China Three Gorges University, Yichang 443002, China
Yanchun Xu: College of Electrical Engineering & New Energy, China Three Gorges University, Yichang 443002, China
Lei Zhang: College of Electrical Engineering & New Energy, China Three Gorges University, Yichang 443002, China
Yue Tong: China Electric Power Research Institute, Wuhan 430074, China

Energies, 2021, vol. 14, issue 6, 1-14

Abstract: 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.

Keywords: frequency response analysis; image processing; decision tree; fully connected neural network (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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