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This paper designs a three-axis closed-loop fluxgate system for geophysical prospecting, which contains excitation, induction, and feedback circuits. By analyzing and comparing the noise levels before and after the system operation, it is concluded that the main factor affecting the fluxgate performance is the excitation circuit noise. By analyzing the principle of fluxgate, the noise on the induction signal and feedback signal can reduce the accuracy of fluxgate and even lead to system instability in serious cases. In this paper, a passive filter circuit is designed to reduce the noise associated with the fundamental frequency from the source to ensure stable operation and improved performance of the system. Simulation and experimental data show that after using the filter circuit, the secondary frequency noise level of the induction signal and the feedback signal noise level are reduced by 10 times, and the trend capturing capability under external magnetic field is greatly improved. [ABSTRACT FROM AUTHOR]

In order to identify the disturbance signal in power system and reduce the influence on system security, a voltage disturbance signal classifier based on improved local mean decomposition (ILMD) and BP neural network is proposed. ILMD is used to decompose the disturbance signal in three layers, and the product function (PF) component with amplitude-frequency information of voltage signal is obtained. The signal energy value constructed by PF component is used as the input of BP neural network to identify and classify the voltage disturbance signal. Experiments on four typical voltage disturbance signals show that the signal classifiers based on ILMD and BP neural networks have high accuracy and good working efficiency for the recognition and classification of voltage disturbance signals. [ABSTRACT FROM AUTHOR]