In the field of current measurement of nuclear fusion devices, dot matrix high current sensors are widely used because of their advantages of high precision, light weight, wide range and low cost. According to the mag...
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In the field of current measurement of nuclear fusion devices, dot matrix high current sensors are widely used because of their advantages of high precision, light weight, wide range and low cost. According to the magnetic field generated by the measured conductor in the circular dot matrix current sensor ring and the Ampere's circuital law, the current value of the measured conductor can be deduced, so as to realize the non-contact current measurement. Because the Ampere's circuital law adopts the line integral equivalent of discrete points, when there are other energized conductors around the measured conductor, the crosstalk field will cause significant measurement errors. In order to solve this problem, a signal processing algorithm should be considered to improve the measurement accuracy and practicability. The effect of the traditional numerical average algorithm is limited by the number of Hall elements, and the convergence factor is difficult to be determined due to the contradiction between the convergence speed and steady state error of the adaptive Least Mean Square (LMS) algorithm. Based on the ideas of the two algorithms mentioned above, this article proposes the wavelet analysis-Kalman algorithm. This algorithm utilizes the known system model and noise statistical characteristics combined with signal estimation and correction to obtain the optimal algorithm parameters, which can further reduce the measurement error of dot matrix current sensor and improve the adaptability of the sensor to the environment. According to the results of simulation and experimental verification, it is concluded that the wavelet analysis-Kalman algorithm is the best among the three algorithms, which can well suppress the influence of crosstalk field and random noise on the measurement results, and greatly improve the measurement accuracy of the sensor.
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