Self-Error Correcting Method for Magnetic-Array-Type Current Sensors in Multi-Core Cable Applications
Xiaohu Liu, Keyu Hou, Kang Ma, Jian Liu, Angang Zheng, Zhengwei Qu, Wei Zhao, Lisha Peng, Songling Huang, Shisong Li

TL;DR
This paper introduces a robust self-error correction method for magnetic-array-type current sensors in multi-core cables, effectively handling phase current variations and significantly improving measurement accuracy.
Contribution
It proposes a novel approach combining phase current decoupling and PCA to transform multi-latent-variable error modeling into a single-variable problem, enhancing sensor error correction.
Findings
Detects sensor error drifts as low as 2×10^{-3} in relative error.
Effectively eliminates overall error drift in magnetic-array sensors.
Validates the method's feasibility and effectiveness through experiments.
Abstract
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…
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Taxonomy
TopicsMagnetic Field Sensors Techniques · Sensor Technology and Measurement Systems · Inertial Sensor and Navigation
