Data-driven measurement performance evaluation of voltage transformers in electric railway traction power supply systems
Abstract Critical for metering and protection in electric railway traction power supply systems (TPSSs), the measurement performance of voltage transformers (VTs) must be timely and reliably monitored. This paper outlines a three-step, RMS data only method for evaluating VTs in TPSSs. First, a kerne...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2025-02-01
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| Series: | Railway Engineering Science |
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| Online Access: | https://doi.org/10.1007/s40534-024-00364-2 |
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| author | Zhaoyang Li Muqi Sun Jun Zhu Haoyu Luo Qi Wang Haitao Hu Zhengyou He Ke Wang |
| author_facet | Zhaoyang Li Muqi Sun Jun Zhu Haoyu Luo Qi Wang Haitao Hu Zhengyou He Ke Wang |
| author_sort | Zhaoyang Li |
| collection | DOAJ |
| description | Abstract Critical for metering and protection in electric railway traction power supply systems (TPSSs), the measurement performance of voltage transformers (VTs) must be timely and reliably monitored. This paper outlines a three-step, RMS data only method for evaluating VTs in TPSSs. First, a kernel principal component analysis approach is used to diagnose the VT exhibiting significant measurement deviations over time, mitigating the influence of stochastic fluctuations in traction loads. Second, a back propagation neural network is employed to continuously estimate the measurement deviations of the targeted VT. Third, a trend analysis method is developed to assess the evolution of the measurement performance of VTs. Case studies conducted on field data from an operational TPSS demonstrate the effectiveness of the proposed method in detecting VTs with measurement deviations exceeding 1% relative to their original accuracy levels. Additionally, the method accurately tracks deviation trends, enabling the identification of potential early-stage faults in VTs and helping prevent significant economic losses in TPSS operations. |
| format | Article |
| id | doaj-art-1f44342f00b54cb4abb652ce7bf2e2ce |
| institution | OA Journals |
| issn | 2662-4745 2662-4753 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Railway Engineering Science |
| spelling | doaj-art-1f44342f00b54cb4abb652ce7bf2e2ce2025-08-20T02:19:57ZengSpringerOpenRailway Engineering Science2662-47452662-47532025-02-0133231132310.1007/s40534-024-00364-2Data-driven measurement performance evaluation of voltage transformers in electric railway traction power supply systemsZhaoyang Li0Muqi Sun1Jun Zhu2Haoyu Luo3Qi Wang4Haitao Hu5Zhengyou He6Ke Wang7National Rail Transit Electrification and Automation Engineering Technology Research Center, Southwest Jiaotong UniversitySchool of Electrical Engineering, Southwest Jiaotong UniversitySchool of Electrical Engineering, Southwest Jiaotong UniversitySchool of Electrical Engineering, Southwest Jiaotong UniversitySchool of Electrical Engineering, Southwest Jiaotong UniversitySchool of Electrical Engineering, Southwest Jiaotong UniversitySchool of Electrical Engineering, Southwest Jiaotong UniversitySchool of Electrical Engineering, Southwest Jiaotong UniversityAbstract Critical for metering and protection in electric railway traction power supply systems (TPSSs), the measurement performance of voltage transformers (VTs) must be timely and reliably monitored. This paper outlines a three-step, RMS data only method for evaluating VTs in TPSSs. First, a kernel principal component analysis approach is used to diagnose the VT exhibiting significant measurement deviations over time, mitigating the influence of stochastic fluctuations in traction loads. Second, a back propagation neural network is employed to continuously estimate the measurement deviations of the targeted VT. Third, a trend analysis method is developed to assess the evolution of the measurement performance of VTs. Case studies conducted on field data from an operational TPSS demonstrate the effectiveness of the proposed method in detecting VTs with measurement deviations exceeding 1% relative to their original accuracy levels. Additionally, the method accurately tracks deviation trends, enabling the identification of potential early-stage faults in VTs and helping prevent significant economic losses in TPSS operations.https://doi.org/10.1007/s40534-024-00364-2Voltage transformerTraction power supply systemMeasurement performanceData-driven evaluationAbrupt change detectionBootstrap confidence interval |
| spellingShingle | Zhaoyang Li Muqi Sun Jun Zhu Haoyu Luo Qi Wang Haitao Hu Zhengyou He Ke Wang Data-driven measurement performance evaluation of voltage transformers in electric railway traction power supply systems Railway Engineering Science Voltage transformer Traction power supply system Measurement performance Data-driven evaluation Abrupt change detection Bootstrap confidence interval |
| title | Data-driven measurement performance evaluation of voltage transformers in electric railway traction power supply systems |
| title_full | Data-driven measurement performance evaluation of voltage transformers in electric railway traction power supply systems |
| title_fullStr | Data-driven measurement performance evaluation of voltage transformers in electric railway traction power supply systems |
| title_full_unstemmed | Data-driven measurement performance evaluation of voltage transformers in electric railway traction power supply systems |
| title_short | Data-driven measurement performance evaluation of voltage transformers in electric railway traction power supply systems |
| title_sort | data driven measurement performance evaluation of voltage transformers in electric railway traction power supply systems |
| topic | Voltage transformer Traction power supply system Measurement performance Data-driven evaluation Abrupt change detection Bootstrap confidence interval |
| url | https://doi.org/10.1007/s40534-024-00364-2 |
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