Optimal Data Selection Rule Mining for Transformer Condition Assessment Article Swipe
Peng Zhang
,
Bo Qi
,
Mengyu Shao
,
Chengrong Li
,
Zhihai Rong
,
Jinxiang Chen
,
Hongbin Wang
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1109/access.2021.3126763
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1109/access.2021.3126763
The condition of the transformer can be accurately assessed based on dissolved gas online monitoring and analysis technology. With the widespread use of Dissolved Gas Analysis (DGA) technology, a large amount of dissolved gas time series data is obtained and forms DGA big data, which brings challenges to the data selection process of traditional condition assessment methods. Inaccurate results might be generated by selecting excessive or insufficient data from a long-span dissolved gas time series. The shorter dissolved gas time series fails to fully characterize the operation law of transformers, and the longer dissolved gas time series contains redundant information that results in inaccurate analysis models and excessive calculation consumption. This paper attempts to mine the optimal data selection rule of long-span dissolved gas time series for more accurate and efficient analysis. In this paper, C-C method was employed to calculate the phase space parameters of the time series. By analyzing the convergence of the correlation integral, the boundary conditions that maintain the time series phase space stability were revealed, and so was the relationship between the optimal length and the embedding dimension. The real cases show that the optimal data set provides the same accuracy and significantly improves the computation speed by 113.54 times. The optimal data selection rule eliminates redundant information in the long-span dissolved gas time series and retains the “optimal data”, which not only preserves the characteristics of the original data, but also improves the computation speed of transformer condition assessment.
Related Topics
Concepts
Dissolved gas analysis
Computation
Time series
Transformer
Computer science
Data mining
Series (stratigraphy)
Algorithm
Engineering
Machine learning
Voltage
Biology
Paleontology
Transformer oil
Electrical engineering
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/access.2021.3126763
- OA Status
- gold
- Cited By
- 8
- References
- 40
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3212592316
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3212592316Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1109/access.2021.3126763Digital Object Identifier
- Title
-
Optimal Data Selection Rule Mining for Transformer Condition AssessmentWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2021Year of publication
- Publication date
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2021-01-01Full publication date if available
- Authors
-
Peng Zhang, Bo Qi, Mengyu Shao, Chengrong Li, Zhihai Rong, Jinxiang Chen, Hongbin WangList of authors in order
- Landing page
-
https://doi.org/10.1109/access.2021.3126763Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1109/access.2021.3126763Direct OA link when available
- Concepts
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Dissolved gas analysis, Computation, Time series, Transformer, Computer science, Data mining, Series (stratigraphy), Algorithm, Engineering, Machine learning, Voltage, Biology, Paleontology, Transformer oil, Electrical engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
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8Total citation count in OpenAlex
- Citations by year (recent)
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2025: 2, 2024: 5, 2022: 1Per-year citation counts (last 5 years)
- References (count)
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40Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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