Next Article in Journal
Dual Auto-Encoder GAN-Based Anomaly Detection for Industrial Control System
Previous Article in Journal
Provider Fairness for Diversity and Coverage in Multi-Stakeholder Recommender Systems
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Applied Sciences
Volume 12
Issue 10
10.3390/app12104985
applsci-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

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

by
Yutao Li
1,
Liliang Wang
1,
Hao Yu
2,
Jiayi An
1,
Yan Pei
1 and
Zheng Qian
1,*
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.
Appl. Sci. 2022, 12(10), 4985; https://doi.org/10.3390/app12104985
Submission received: 14 April 2022 / Revised: 10 May 2022 / Accepted: 13 May 2022 / Published: 14 May 2022
Browse Figures
Versions Notes

Abstract

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%.
Keywords: TMR; dynamic hysteresis; probability; Preisach TMR; dynamic hysteresis; probability; Preisach

Share and Cite

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://doi.org/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://doi.org/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://doi.org/10.3390/app12104985

APA Style

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

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop