Hilbert ID Considering Multi-Window Feature Extraction for Transformer Deep Vision Fault Positioning

Hilbert ID Considering Multi-Window Feature Extraction for Transformer Deep Vision Fault Positioning

Most of the power transformer fault diagnostic researches so far focuses on its fault type diagnosis, but there are less related researches on fault positioning, and the diagnostic methods of which are still less intelligent. This paper proposes a two-dimensional Hilbert ID considering multi-window...

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Bibliographic Details
Main Authors: Xiaoxin Wu, Yigang He, Chenyuan Wang, Wenjie Wu, Chuankun Wang, Jiajun Duan
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Convolutional neural network (CNN)
deep transfer learning (DTL)
fault positioning
Hilbert visualization
multi-window feature extraction
power transformer
Online Access:https://ieeexplore.ieee.org/document/9084158/

Internet

https://ieeexplore.ieee.org/document/9084158/

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