Power transformer fault diagnosis considering data imbalance and data set fusion-Reference-Cited by-同舟云学术

Power transformer fault diagnosis considering data imbalance and data set fusion

Published:2020-12-09 Issue:3 Volume:6 Page:543-554
ISSN:2397-7264
Container-title:High Voltage
language:en
Short-container-title:High Voltage

Author:

Zhang Yang¹^ORCID,Chen Hong Cai²^ORCID,Du Yaping¹^ORCID,Chen Min³,Liang Jie⁴,Li Jianhong⁴,Fan Xiqing⁵,Yao Xin⁶^ORCID

Affiliation:

1. Department of Building Service Engineering Hong Kong Polytechnic University Hong Kong China

2. Academy for Advanced Interdisciplinary Studies and Department of Electrical and Electronic Engineering Southern University of Science and Technology Shenzhen China

3. Research Institute State Grid Zhejiang Electric Power Co., Ltd Hangzhou China

4. Zhejiang Huayun Information Technology Co., Ltd Hangzhou China

5. State Grid Zhejiang Electric Power Co., Ltd Lishui Power Supply Bureau Lishui China

6. Guangdong Provincial Key Laboratory of Brain‐inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China

Publisher

Institution of Engineering and Technology (IET)

Subject

Electrical and Electronic Engineering,Energy Engineering and Power Technology

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1049/hve2.12059

Reference53 articles.

1. Review of condition assessment of power transformers in service

2. Causes of transformer failures and diagnostic methods – A review

3. Condition monitoring and diagnosis of power equipment: review and prospective

4. Method of inter‐turn fault detection for next‐generation smart transformers based on deep learning algorithm

5. Proposed methodology for online frequency response analysis based on magnetic coupling to detect winding deformations in transformers

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