摘要详情
861 / 2022-06-15 15:52:27
A filter-based Semi-supervised feature selection approach for power transformer fault diagnosis based on Dissolved Gas Analysis
Dissolved Gas Analysis,fault diagnosis,semi- supervised learning,feature selection
全文录用
Collecting labeled Dissolved Gas Analysis (DGA) data is difficult because the determination of the transformer fault is time-consuming and expensive in the transformer substation, but DGA data without labels is easier to obtain. In order to make full use of DGA data with few labels and a large number of unlabeled data to improve the transformer fault diagnosis rate, it is an important to study the DGA fault diagnosis method based on semi-supervised learning for solving practical problems in the field. Therefore, the paper proposed a novel filter-based semi-supervised feature selection method for building fault diagnosis model and selecting optimal DGA features. The method is test by using IEC T10 dataset and compared with traditional supervised diagnostic models. The results show that the proposed method works in optimizing DGA features and has strong robustness in solving small sample DGA classification problems.
重要日期
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会议日期
09月25日
2022
至09月29日
2022
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08月15日 2022
提前注册日期
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09月10日 2022
报告提交截止日期
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11月10日 2022
注册截止日期
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11月30日 2022
初稿截稿日期
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11月30日 2022
终稿截稿日期
主办单位
IEEE DEIS
承办单位
Chongqing University
联系方式
- Zhengyong Huang
- 61******@qq.com
历届会议
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2020年09月06日 中国 Beijing
2020 IEEE International Conference on High Voltage Engineering and Application -
2018年09月10日 希腊
2018 IEEE International Conference on High Voltage Engineering and Application -
2016年09月19日 中国 Chengdu, China
2016 IEEE International Conference on High Voltage Engineering and Application
