Soft Calibration of Online DGA Sensors Using Semi-Supervised S3TR for Power Transformer
Dissolved gas analysis (DGA) is one of the key techniques for the condition monitoring of power transformers. With the development of online DGA sensors, condition monitoring has shifted from manual to online. However, these sensors are not as accurate as expected, so they may provide inaccurate eve...
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Published in | IEEE transactions on instrumentation and measurement Vol. 73; pp. 1 - 10 |
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Main Authors | , , |
Format | Journal Article |
Language | English |
Published |
New York
IEEE
2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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Abstract | Dissolved gas analysis (DGA) is one of the key techniques for the condition monitoring of power transformers. With the development of online DGA sensors, condition monitoring has shifted from manual to online. However, these sensors are not as accurate as expected, so they may provide inaccurate even misleading maintenance suggestions. To ensure the accuracy of data, these sensors are required to be carefully calibrated. Thus, a novel method is proposed to calibrate online sensors using manually measured data. However, the calibration is not an easy task because manual measurements are evaluated much sparser than online sensor (more than 1 month versus 15 min), leading to a training set containing a few labeled samples (manual data) and a large amount of unlabeled samples (online data). To deal with such a dataset, a semi-supervised safe tri-training regression (S3TR) is proposed to build the calibration model. The proposed method adds reliable labels to the data during the training, thus easing the difficulty of the dataset. The efficacy of the proposed method is demonstrated by evaluating a DGA dataset collected from in-service power transformers. The superiority of the proposed method is confirmed through comparing it with popular calibration methods. |
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AbstractList | Dissolved gas analysis (DGA) is one of the key techniques for the condition monitoring of power transformers. With the development of online DGA sensors, condition monitoring has shifted from manual to online. However, these sensors are not as accurate as expected, so they may provide inaccurate even misleading maintenance suggestions. To ensure the accuracy of data, these sensors are required to be carefully calibrated. Thus, a novel method is proposed to calibrate online sensors using manually measured data. However, the calibration is not an easy task because manual measurements are evaluated much sparser than online sensor (more than 1 month versus 15 min), leading to a training set containing a few labeled samples (manual data) and a large amount of unlabeled samples (online data). To deal with such a dataset, a semi-supervised safe tri-training regression (S3TR) is proposed to build the calibration model. The proposed method adds reliable labels to the data during the training, thus easing the difficulty of the dataset. The efficacy of the proposed method is demonstrated by evaluating a DGA dataset collected from in-service power transformers. The superiority of the proposed method is confirmed through comparing it with popular calibration methods. |
Author | Zhang, Yang Chen, Min Chen, Hong Cai |
Author_xml | – sequence: 1 givenname: Hong Cai orcidid: 0000-0001-5418-4122 surname: Chen fullname: Chen, Hong Cai email: chenhc@seu.edu.cn organization: School of Automation and Key Laboratory of Measurement and Control of CSE, Ministry of Education, Southeast University, Nanjing, China – sequence: 2 givenname: Yang orcidid: 0000-0003-0156-8952 surname: Zhang fullname: Zhang, Yang email: 16103271g@connect.polyu.hk organization: College of Advanced Interdisciplinary Studies, National University of Defense Technology, Changsha, China – sequence: 3 givenname: Min orcidid: 0000-0003-0998-7297 surname: Chen fullname: Chen, Min email: chenmin@zj.sgcc.com.cn organization: Research Institute, State Grid Zhejiang Electric Power Company Ltd., Hangzhou, China |
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SubjectTerms | Calibration Condition monitoring Datasets Dissolved gas analysis (DGA) Dissolved gases Gas analysis Manuals Oil insulation Oils Power transformers semi-supervised regression sensor calibration Sensors small data Training transformer diagnosis Transformers |
Title | Soft Calibration of Online DGA Sensors Using Semi-Supervised S3TR for Power Transformer |
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