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

TL;DR

The paper tackles online error-state evaluation and long-term stability for magnetic-array-type current sensors in multi-core cables, where time-varying phase-current correlations degrade traditional multi-latent-variable models. It introduces a robust self-error correcting framework that decouples phase currents and applies PCA to achieve a single-latent-variable representation per phase, enabling reliable drift detection via the $Q$ statistic and identification of drifted sensors. Drift magnitudes are quantified through a bi-objective NSGA-II optimization to minimize amplitude and phase correlation metrics, followed by compensating the identified sensors and reconstructing phase currents. Experimental validation with an eight-sensor array on a three-core cable demonstrates drift detection down to relative errors of $2 imes10^{-3}$ and phase errors of $2 imes10^{-3}$ rad, confirming substantial reduction of overall array error and proving feasibility for real-time, non-contact current sensing in complex conductor systems. The findings also highlight practical requirements, including a minimum sensor count and uniform distribution, to achieve robust self-error correction in operational environments.

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.

Figures (14)

• Figure 1: Non-contact current measurement for multi-core cables using a magnetic sensor array, with rectangles representing magnetic field sensors and arrows indicating their sensitivity orientations.
• Figure 2: Principle of the proposed self-error correcting method for magnetic-array-type current sensors applied to multi-core cables.
• Figure 3: Illustration of phase currents derivation from measurements of different sub-combinations of magnetic field sensors.
• Figure 4: Flow chart for identification of the error-drifted magnetic field sensors.
• Figure 5: Schematic of the self-error correction experiment and corresponding physical test platform (b).