期刊
ENERGIES
卷 14, 期 6, 页码 -出版社
MDPI
DOI: 10.3390/en14061531
关键词
frequency response analysis; image processing; decision tree; fully connected neural network
资金
- Key Project of Science and Technology Research Plan of Education Department of Hubei [D20201203]
- Natural Science Foundation of Hubei Province [2018CFB189]
This paper presents a method for reliable analysis of transformer FRA signatures based on a decision tree classification model and a fully connected neural network, showing good performance in both training and validation stages.
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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