Self-Error Correcting Method for Magnetic-Array-Type Current Sensors in Multi-Core Cable Applications

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 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.