Running Status Diagnosis of Onboard Traction Transformers Based on Kernel Principal Component Analysis and Fuzzy Clustering
The onboard traction transformer is a critical equipment of high-speed trains, its running state directly affects the safety and stability of a train’s operation. Given the complexity of the running condition of the onboard traction transformer, this paper proposes a running state diagnos...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9524634/ |
| _version_ | 1818602287756476416 |
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| author | Junmin Zhu Shuaibing Li Haiying Dong |
| author_facet | Junmin Zhu Shuaibing Li Haiying Dong |
| author_sort | Junmin Zhu |
| collection | DOAJ |
| description | The onboard traction transformer is a critical equipment of high-speed trains, its running state directly affects the safety and stability of a train’s operation. Given the complexity of the running condition of the onboard traction transformer, this paper proposes a running state diagnosis algorithm based on kernel principal component analysis (KPCA) and fuzzy clustering. To fully extract the status information of the onboard traction transformer, the aging characteristics of insulating oil and main insulation are analyzed under different running mileage as the first step. Thereby, to eliminate the signal redundancy, the status feature set of the onboard traction transformer is analyzed by KPCA combined with the characteristic quantities of the traditional dissolved gas analysis (DGA), and the eigenvalues with the contribution rate of over 95% are used as new eigenvectors. Finally, a status diagnosis model is established by using fuzzy clustering analysis, considering the limitations of fault data of onboard traction transformer. The results from field collected data show that the proposed method is effective in diagnosing the running status of the onboard traction transformer. |
| first_indexed | 2024-12-16T13:04:53Z |
| format | Article |
| id | doaj.art-d097a3cc633f4ea484c6af1d067141f3 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-16T13:04:53Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-d097a3cc633f4ea484c6af1d067141f32022-12-21T22:30:45ZengIEEEIEEE Access2169-35362021-01-01912183512184410.1109/ACCESS.2021.31083459524634Running Status Diagnosis of Onboard Traction Transformers Based on Kernel Principal Component Analysis and Fuzzy ClusteringJunmin Zhu0https://orcid.org/0000-0003-0107-2009Shuaibing Li1https://orcid.org/0000-0002-0680-5796Haiying Dong2https://orcid.org/0000-0002-3909-3246School of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou, ChinaSchool of New Energy and Power Engineering, Lanzhou Jiaotong University, Lanzhou, ChinaSchool of New Energy and Power Engineering, Lanzhou Jiaotong University, Lanzhou, ChinaThe onboard traction transformer is a critical equipment of high-speed trains, its running state directly affects the safety and stability of a train’s operation. Given the complexity of the running condition of the onboard traction transformer, this paper proposes a running state diagnosis algorithm based on kernel principal component analysis (KPCA) and fuzzy clustering. To fully extract the status information of the onboard traction transformer, the aging characteristics of insulating oil and main insulation are analyzed under different running mileage as the first step. Thereby, to eliminate the signal redundancy, the status feature set of the onboard traction transformer is analyzed by KPCA combined with the characteristic quantities of the traditional dissolved gas analysis (DGA), and the eigenvalues with the contribution rate of over 95% are used as new eigenvectors. Finally, a status diagnosis model is established by using fuzzy clustering analysis, considering the limitations of fault data of onboard traction transformer. The results from field collected data show that the proposed method is effective in diagnosing the running status of the onboard traction transformer.https://ieeexplore.ieee.org/document/9524634/Onboard traction transformerrunning status diagnosisinsulation agingkernel principal component analysisfuzzy clustering |
| spellingShingle | Junmin Zhu Shuaibing Li Haiying Dong Running Status Diagnosis of Onboard Traction Transformers Based on Kernel Principal Component Analysis and Fuzzy Clustering IEEE Access Onboard traction transformer running status diagnosis insulation aging kernel principal component analysis fuzzy clustering |
| title | Running Status Diagnosis of Onboard Traction Transformers Based on Kernel Principal Component Analysis and Fuzzy Clustering |
| title_full | Running Status Diagnosis of Onboard Traction Transformers Based on Kernel Principal Component Analysis and Fuzzy Clustering |
| title_fullStr | Running Status Diagnosis of Onboard Traction Transformers Based on Kernel Principal Component Analysis and Fuzzy Clustering |
| title_full_unstemmed | Running Status Diagnosis of Onboard Traction Transformers Based on Kernel Principal Component Analysis and Fuzzy Clustering |
| title_short | Running Status Diagnosis of Onboard Traction Transformers Based on Kernel Principal Component Analysis and Fuzzy Clustering |
| title_sort | running status diagnosis of onboard traction transformers based on kernel principal component analysis and fuzzy clustering |
| topic | Onboard traction transformer running status diagnosis insulation aging kernel principal component analysis fuzzy clustering |
| url | https://ieeexplore.ieee.org/document/9524634/ |
| work_keys_str_mv | AT junminzhu runningstatusdiagnosisofonboardtractiontransformersbasedonkernelprincipalcomponentanalysisandfuzzyclustering AT shuaibingli runningstatusdiagnosisofonboardtractiontransformersbasedonkernelprincipalcomponentanalysisandfuzzyclustering AT haiyingdong runningstatusdiagnosisofonboardtractiontransformersbasedonkernelprincipalcomponentanalysisandfuzzyclustering |