An Adaptive Eccentricity Correction Method for Arrayed Single‐Axis TMR Current Sensors

Abstract

Current sensors based on the tunneling magnetoresistive effect (TMR) are widely used for current measurement due to their high sensitivity, small size, and low power consumption. This paper proposes an effective error correction model to rectify the eccentricity of the transmission line, which can cause a significant measurement error in the ring‐array single‐axis TMR sensor. The model employs a convolutional neural network (CNN) to identify the relationship between the conductor eccentricity and the output of three sensors. The resulting correction factor is then fed back to eliminate the error associated with wire eccentricity. Concurrently, the Sparrow search algorithm (SSA) is employed to optimize the hyperparameters of the convolutional neural network (CNN) in order to enhance the model's performance. The experimental results demonstrate that the maximum error of the ring‐array single‐axis TMR current sensor, corrected by SSA‐CNN, is less than 0.42%, which markedly enhances the precision of the measurement. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.