Design of Fluxgate Current Sensor Based on Magnetization Residence Times and Neural Networks

Design of Fluxgate Current Sensor Based on Magnetization Residence Times and Neural Networks

Blog Article

This study introduces a novel fluxgate current sensor markbroyard.com with a compact, ring-shaped configuration that exhibits improved performance through the integration of magnetization residence times and neural networks.The sensor distinguishes itself with a unique magnetization profile, denoted as M waves, which emerge from the interaction between the target signal and ambient magnetic interference, effectively enhancing interference suppression.These M waves highlight the non-linear coupling between the magnetic field and magnetization residence times.

Detection of these residence times is accomplished using full-wave rectification circuits and a Schmitt trigger, with a digital output provided by timing sequence detection.A dual-layer feedforward neural network deciphers the target signal, exploiting this non-linear relationship.The sensor achieves a linearity error of 0.

054% within a measurement valhalla axys range of 15 A.When juxtaposed with conventional sensors utilizing the residence-time difference strategy, our sensor reduces linearity error by more than 40-fold and extends the effective measurement range by 150%.Furthermore, it demonstrates a significant decrease in ambient magnetic interference.