Saved in:
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2512.07521 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866911308119539712 |
|---|---|
| author | Liu, Xiaohu Hou, Keyu Ma, Kang Liu, Jian Zheng, Angang Qu, Zhengwei Zhao, Wei Peng, Lisha Huang, Songling Li, Shisong |
| author_facet | Liu, Xiaohu Hou, Keyu Ma, Kang Liu, Jian Zheng, Angang Qu, Zhengwei Zhao, Wei Peng, Lisha Huang, Songling Li, Shisong |
| contents | Data-driven methods enable online assessment of error states in magnetic-array-type current sensors, and long-term measurement stability can be enhanced through further self-error correction. However, when the magnetic-array-type current sensors are applied to multi-conductor systems such as multi-core cables, the time-varying correlations among conductor currents may degrade the performance of multi-latent-variable data-driven models for error evaluation. To address this issue, this paper proposes a robust self-error correcting method for magnetic-array-type current sensors even under significant variations in phase current correlations (e.g., large fluctuations in three-phase current imbalance). By incorporating phase current decoupling and principal component analysis (PCA), the correlation analysis of multi-latent variables (i.e., multi-conductor currents) is transformed into a single-latent-variable (corresponding to single phase current) modeling problem. Experimental results demonstrate that the proposed method effectively detects error drifts of magnetic field sensors as low as $2\times10^{-3}$ in relative error and $2\times10^{-3}$ rad in phase error. Accurate evaluation and correction of each magnetic field sensor's error drifts substantially eliminates the overall error drift in the magnetic-array-type current sensor, validating the feasibility and effectiveness of the proposed self-error correcting method. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_07521 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Self-Error Correcting Method for Magnetic-Array-Type Current Sensors in Multi-Core Cable Applications Liu, Xiaohu Hou, Keyu Ma, Kang Liu, Jian Zheng, Angang Qu, Zhengwei Zhao, Wei Peng, Lisha Huang, Songling Li, Shisong Instrumentation and Detectors Data-driven methods enable online assessment of error states in magnetic-array-type current sensors, and long-term measurement stability can be enhanced through further self-error correction. However, when the magnetic-array-type current sensors are applied to multi-conductor systems such as multi-core cables, the time-varying correlations among conductor currents may degrade the performance of multi-latent-variable data-driven models for error evaluation. To address this issue, this paper proposes a robust self-error correcting method for magnetic-array-type current sensors even under significant variations in phase current correlations (e.g., large fluctuations in three-phase current imbalance). By incorporating phase current decoupling and principal component analysis (PCA), the correlation analysis of multi-latent variables (i.e., multi-conductor currents) is transformed into a single-latent-variable (corresponding to single phase current) modeling problem. Experimental results demonstrate that the proposed method effectively detects error drifts of magnetic field sensors as low as $2\times10^{-3}$ in relative error and $2\times10^{-3}$ rad in phase error. Accurate evaluation and correction of each magnetic field sensor's error drifts substantially eliminates the overall error drift in the magnetic-array-type current sensor, validating the feasibility and effectiveness of the proposed self-error correcting method. |
| title | Self-Error Correcting Method for Magnetic-Array-Type Current Sensors in Multi-Core Cable Applications |
| topic | Instrumentation and Detectors |
| url | https://arxiv.org/abs/2512.07521 |