Presentation Details
A filter-based Semi-supervised feature selection approach for power transformer fault diagnosis based on Dissolved Gas Analysis
ID：266
View Protection：ATTENDEE
Updated Time：2022-08-29 15:50:34 Hits：330
Poster Presentation
Start Time：Pending（Asia/Shanghai）
Duration：Pending
Session：No Session »
Abstract
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.
Keywords
Dissolved Gas Analysis,fault diagnosis,semi- supervised learning,feature selection
Speaker
Xuemin Tan
College of Automation；Chengdu University of Information TechnologySubmission Author
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Important Date
-
Conference Date
Sep 25
2022
toSep 29
2022
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Aug 15 2022
Early Bird Registration
-
Sep 10 2022
Contribution Submission Deadline
-
Nov 10 2022
Registration deadline
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Nov 30 2022
Draft paper submission deadline
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Nov 30 2022
Final Paper Deadline
Sponsored By
IEEE DEIS
Organized By
Chongqing University
Contact Information
- Zhengyong Huang
- 61******@qq.com
Previous Conferences
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2020-09-06 China Beijing
2020 IEEE International Conference on High Voltage Engineering and Application -
2018-09-10 Greece
2018 IEEE International Conference on High Voltage Engineering and Application -
2016-09-19 China Chengdu, China
2016 IEEE International Conference on High Voltage Engineering and Application
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