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Article

A Dynamic Hysteresis Model for TMR-Current Sensors Based on Probability Estimation of Hysteresis Operator and Its Switching Time

1
School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China
2
High Voltage Research Institute, China Electric Power Research Institute, Beijing 100089, China
*
Author to whom correspondence should be addressed.
Academic Editor: Francesco Dell’Olio
Received: 14 April 2022 / Revised: 10 May 2022 / Accepted: 13 May 2022 / Published: 14 May 2022
Hysteresis is one of the main factors affecting the measurement accuracy of TMR sensors, especially in dynamic measurements. The commonly used Preisach hysteresis compensation model has some problems, such as complex modeling and difficulty in accurately measuring the step time, resulting in low accuracy in dynamic measurements. In this paper, considering the distribution characteristics of the conversion time of the hysteresis operator in dynamic measurements, a dynamic hysteresis model based on the probability estimation of the hysteresis operator and its conversion time is proposed. Compared with the existing methods, this method only needs to calculate the distribution of the sensor hysteresis operator to realize the calculation of hysteresis characteristics without a physical model or fitting algorithm. It has good generalization performance and a corresponding fast speed. In the test of two typical TMR sensors, compared with the transmission Preisach model, the maximum error of this method is reduced by 46.7%, the variance can be reduced by 90.2%, and the average value can be reduced by 65.1%. View Full-Text
Keywords: TMR; dynamic hysteresis; probability; Preisach TMR; dynamic hysteresis; probability; Preisach
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MDPI and ACS Style

Li, Y.; Wang, L.; Yu, H.; An, J.; Pei, Y.; Qian, Z. A Dynamic Hysteresis Model for TMR-Current Sensors Based on Probability Estimation of Hysteresis Operator and Its Switching Time. Appl. Sci. 2022, 12, 4985. https://0-doi-org.brum.beds.ac.uk/10.3390/app12104985

AMA Style

Li Y, Wang L, Yu H, An J, Pei Y, Qian Z. A Dynamic Hysteresis Model for TMR-Current Sensors Based on Probability Estimation of Hysteresis Operator and Its Switching Time. Applied Sciences. 2022; 12(10):4985. https://0-doi-org.brum.beds.ac.uk/10.3390/app12104985

Chicago/Turabian Style

Li, Yutao, Liliang Wang, Hao Yu, Jiayi An, Yan Pei, and Zheng Qian. 2022. "A Dynamic Hysteresis Model for TMR-Current Sensors Based on Probability Estimation of Hysteresis Operator and Its Switching Time" Applied Sciences 12, no. 10: 4985. https://0-doi-org.brum.beds.ac.uk/10.3390/app12104985

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