The sensor tag in this article uses passive wireless sensing technology to transmit data and energy. It uses the built-in sensing module to detect the magnetic field intensity around the cable, realizing cable current measurement without battery maintenance and cable connection. However, due to the limitation of microwatt power supplied by radio frequency waves, the number of sampling points of the sensor tag is restricted, the sampling frequency is low and the nonlinearity of the magnetic field sensitive element makes the mapping relationship between the sensor data and the cable current complicated. Hence, it is difficult to calibrate and measure the sensor tag. To solve this problem, this article proposes an optimization algorithm combining particle swarm optimization and interior point method to extract the characteristic values (frequency and effective value) from the sensing data by Fourier series fitting and calibration. During the measurement, the characteristic values are obtained by Fourier series fitting of sensor data groups. Then, the characteristic values are substituted into the sensor model established during calibration. The constant amplitude current measurement with the frequency of 47~ 60 Hz and an effective value of 5~ 45 A is realized, and the relative errors of current frequency and effective value are less than 0. 4% and 1. 9% , respectively. The experimental results show that the measurement system equipped with the sensor tag can not only realize passive wireless measurement of cable current, but also can realize the maximum current fluctuation measurement allowed by domestic power system standards.