AC Current Sensor Based on Magnetoelectric Composites Transducers: Resonance Damping, Current Dynamic, and Electric Field Disturbances

In this article, a current sensor based on a magnetoelectric (ME) composite realized by electrodeposition is presented. This ME sample is inserted in the gap of a toric flux concentrator associated with permanent magnets. The current dynamic and the current sensor bandwidth are studied. Then, mechanical damping allows the absorption of the mechanical resonance that affects the current measurement. Finally, a device is set up to test the sensor’s immunity to electric field disturbances. An electronic based on charge preamplifiers connected in differential mode strongly attenuates these disturbances.

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Publication Details

Journal
IEEE Transactions on Magnetics
Published
2024-08-21
DOI
https://doi.org/10.1109/tmag.2024.3446910
Primary Topic
Neural Networks and Applications
Type
article
Field-Weighted Citation Impact
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article

AC Current Sensor Based on Magnetoelectric Composites Transducers: Resonance Damping, Current Dynamic, and Electric Field Disturbances

F. Mazaleyrat, Raphaël Filâtre, Vincent Loyau
IEEE Transactions on Magnetics
Neural Networks and Applications
article

AC Current Sensor Based on Magnetoelectric Composites Transducers: Resonance Damping, Current Dynamic, and Electric Field Disturbances

F. Mazaleyrat, Raphaël Filâtre, Vincent Loyau
article en

Abstract

In this article, a current sensor based on a magnetoelectric (ME) composite realized by electrodeposition is presented. This ME sample is inserted in the gap of a toric flux concentrator associated with permanent magnets. The current dynamic and the current sensor bandwidth are studied. Then, mechanical damping allows the absorption of the mechanical resonance that affects the current measurement. Finally, a device is set up to test the sensor’s immunity to electric field disturbances. An electronic based on charge preamplifiers connected in differential mode strongly attenuates these disturbances.

IEEE Transactions on MagneticsVol. 61(6)
Centre National de la Recherche Scientifique (FR), Université Paris-Saclay (FR), Laboratoire des systèmes et applications des technologies de l'information et de l'énergie (FR)
Affordable and clean energy
Openalex Percentile: Top 89%
Neural Networks and Applications
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