Multi-Sensor Fusion Method and Realization Based on Hall and GMR
At present,Hall and GMR are widely used in power system current measurement. In order to give full play to their advantages and reduce their limitations,a multi-sensor fusion scheme based on Hall and GMR is proposed based on the analysis of temperature char-acteristics,noise characteristics and measured current range of Hall and GMR. Based on the definition of the sensitivity difference(ΔS)be-tween GMR and Hall,the fusion domain composed of the measured current(Ⅰ) and sensitivity difference(ΔS) is divided into four domains. In domain Ⅱ,the multi-sensing weighted observation fusion Kalman filtering algorithm is used to fuse the observation quantity and ob-servation noise of Hall and GMR together with the state equation to perform Kalman filtering. In domain Ⅰ,the optimal weight assign-ment method of data weighted fusion is adopted,which assigns a larger weight to the measured data of Hall and a smaller weight to the measured data of GMR. In domain Ⅲ,the weight allocation is opposite,and the smooth transition of data fusion can be achieved among domains. Based on the multi-sensor fusion method,a combined closed-loop current sensor is designed,including the design and simula-tion of the magnetic core and circuit. Simulation and prototype experiment results show that the root mean square error between the fu-sion value and the real value is as low as 0.004 in domain Ⅱ. In domain Ⅰ and Ⅲ,the relative errors(Ei) are below 0.255％. Compared with the single sensor method,the multi-sensor fusion method increases the current measurement range of the combined sensor,which is suitable for the scene with large temperature variation range,and the current measurement accuracy and credibility are higher.
multi-sensor data fusionHallGMRdistributed weighted observation fusionadaptive Kalman filteringthe optimal weights
万方数据
维普
