Rapid Transformer Health State Recognition Through Canopy Cluster-Merging of Dissolved Gas Data in High-Dimensional Space Article Swipe
Bo Qi
,
Peng Zhang
,
Zhihai Rong
,
Jianyi Wang
,
Chengrong Li
,
Jinxiang Chen
·
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.1109/access.2019.2928628
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.1109/access.2019.2928628
Dissolved gases in oil are major parameters for assessing the health state of power transformers. Recognizing that the existing state recognition methods are often restrained by data fluctuation and usually require greater computational load, this paper proposes a rapid transformer health state recognition method through Canopy cluster-merging of dissolved gas data in high-dimensional space. Following the introduction of fluctuation coefficient to evaluate the quality of data and the assignation of weight to reflect the difference between gases, the variable-weighted high-dimensional space of dissolved gases is established to suppress the impact from the fluctuated data. The novel Canopy cluster-merging method that overcomes the instability and high computational complexity of the conventional clustering method is then proposed and used in the variable-weighted high-dimensional space to recognize the abnormal state transformer. Applying the state recognition rules and matching with the established abnormal event base, the health state of the transformer could be rapidly recognized. The single case verification concludes that the proposed method has better clustering effect and can significantly improve the clustering speed even by 17.08 times. The group verification test indicates that the proposed method not only demonstrates an accuracy as high as 91.43% but also shows extremely high efficiency compared with the conventional recognition methods.
Related Topics
Concepts
Cluster analysis
Transformer
Computer science
Dissolved gas analysis
Data mining
Transformer oil
Pattern recognition (psychology)
Artificial intelligence
Algorithm
Engineering
Voltage
Electrical engineering
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/access.2019.2928628
- https://ieeexplore.ieee.org/ielx7/6287639/8600701/08762157.pdf
- OA Status
- gold
- Cited By
- 6
- References
- 48
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2960947282
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W2960947282Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1109/access.2019.2928628Digital Object Identifier
- Title
-
Rapid Transformer Health State Recognition Through Canopy Cluster-Merging of Dissolved Gas Data in High-Dimensional SpaceWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2019Year of publication
- Publication date
-
2019-01-01Full publication date if available
- Authors
-
Bo Qi, Peng Zhang, Zhihai Rong, Jianyi Wang, Chengrong Li, Jinxiang ChenList of authors in order
- Landing page
-
https://doi.org/10.1109/access.2019.2928628Publisher landing page
- PDF URL
-
https://ieeexplore.ieee.org/ielx7/6287639/8600701/08762157.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://ieeexplore.ieee.org/ielx7/6287639/8600701/08762157.pdfDirect OA link when available
- Concepts
-
Cluster analysis, Transformer, Computer science, Dissolved gas analysis, Data mining, Transformer oil, Pattern recognition (psychology), Artificial intelligence, Algorithm, Engineering, Voltage, Electrical engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
6Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 2, 2021: 1, 2020: 3Per-year citation counts (last 5 years)
- References (count)
-
48Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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