LAPSE:2023.30192
Published Article
LAPSE:2023.30192
Fault Diagnosis of Transformer Windings Based on Decision Tree and Fully Connected Neural Network
April 14, 2023
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.
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.
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Keywords
decision tree, frequency response analysis, fully connected neural network, image processing
Subject
Suggested Citation
Li Z, Zhang Y, Abu-Siada A, Chen X, Li Z, Xu Y, Zhang L, Tong Y. Fault Diagnosis of Transformer Windings Based on Decision Tree and Fully Connected Neural Network. (2023). LAPSE:2023.30192
Author Affiliations
Li Z: College of Electrical Engineering & New Energy, China Three Gorges University, Yichang 443002, China; Hubei Provincial Key Laboratory for Operation and Control of Cascaded Hydropower Station, China Three Gorges University, Yichang 443002, China [ORCID]
Zhang Y: College of Electrical Engineering & New Energy, China Three Gorges University, Yichang 443002, China
Abu-Siada A: Department of Electrical and Computer Engineering, Curtin University, Perth 6000, Australia [ORCID]
Chen X: College of Electrical Engineering & New Energy, China Three Gorges University, Yichang 443002, China
Li Z: College of Electrical Engineering & New Energy, China Three Gorges University, Yichang 443002, China
Xu Y: College of Electrical Engineering & New Energy, China Three Gorges University, Yichang 443002, China
Zhang L: College of Electrical Engineering & New Energy, China Three Gorges University, Yichang 443002, China
Tong Y: China Electric Power Research Institute, Wuhan 430074, China
Zhang Y: College of Electrical Engineering & New Energy, China Three Gorges University, Yichang 443002, China
Abu-Siada A: Department of Electrical and Computer Engineering, Curtin University, Perth 6000, Australia [ORCID]
Chen X: College of Electrical Engineering & New Energy, China Three Gorges University, Yichang 443002, China
Li Z: College of Electrical Engineering & New Energy, China Three Gorges University, Yichang 443002, China
Xu Y: College of Electrical Engineering & New Energy, China Three Gorges University, Yichang 443002, China
Zhang L: College of Electrical Engineering & New Energy, China Three Gorges University, Yichang 443002, China
Tong Y: China Electric Power Research Institute, Wuhan 430074, China
Journal Name
Energies
Volume
14
Issue
6
First Page
1531
Year
2021
Publication Date
2021-03-10
ISSN
1996-1073
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Original Submission
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PII: en14061531, Publication Type: Journal Article
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Published Article
LAPSE:2023.30192
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https://doi.org/10.3390/en14061531
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Apr 14, 2023
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