Fuzzy Classification Method for Transformer Faults Based on Improved FCM
Dissolved gas analysis (DGA) in transformer oil is an important technology to identify transformer fault types, and fuzzy clustering is an effective analysis method. However, the traditional fuzzy clustering algorithm is sensitive to the randomly initialized cluster center and the effective meas...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Harbin University of Science and Technology Publications
2023-08-01
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| Series: | Journal of Harbin University of Science and Technology |
| Subjects: | |
| Online Access: | https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=2240 |
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| Summary: | Dissolved gas analysis (DGA) in transformer oil is an important technology to identify transformer fault types, and fuzzy clustering is an effective analysis method. However, the traditional fuzzy clustering algorithm is sensitive to the randomly initialized cluster center and the effective measurement range of membership function is small, which makes it easy to fall into local extremum points, so the actual classification effect is not good. To address the shortcomings of the traditional FCM. Canopy algorithm was first used to conduct rough clustering of DGA data, and the results were used as the initial cluster center and optimal cluster number of subsequent FCM clustering, which reduced the subjectivity of artificial and random initialization parameters. Then, the iterative function of FCM membership was reconstructed by introducing the similarity index in the form of negative exponential function, which reduced the possibility of the algorithm falling into local extremum points. Finally, the effectiveness and practicability of the improved algorithm in transformer fault classification are verified by analyzing the fault gas data. |
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| ISSN: | 1007-2683 |