journal article·2022-03-01Open access

Accurate small major hysteresis loops calculation by the Preisach model with improved parametric distribution function

Long Chen, Tairan Zhang, Tong Ben, Peng Wei

AIP Advances· Volume 12· Issue 3

Abstract

The inversed Preisach hysteresis model is widely used to realize the rapid coupling of hysteresis characteristics of core materials in finite element analysis. However, the small major hysteresis loop is difficult to be accurately simulated. In this paper, an improved inversed Preisach model based on an improved parametric distribution function is proposed. By analyzing the magnetic remanence, the waveform factor is coupled with the parametric distribution function, and the identification of the small major hysteresis loop is strengthened. The distribution function parameters can be found from the limit hysteresis loop through the optimization algorithm. In the numerical calculation process of the model, the amplitude correction coefficient in the discrete formula is extracted by the basic magnetization curve. Compared with the previous method, this method simulates the limit hysteresis loop well and has higher simulation accuracy for the small major hysteresis loops.
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Research context

OpenAlex topics: Magnetic Properties and Applications · Electric Motor Design and Analysis · Microstructure and Mechanical Properties of Steels

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Hubei Provincial Engineering Technology Research Center for Power Transmission Line, China Three Gorges University 1 , Yichang 443002, China

College of Electrical Engineering and New Energy, China Three Gorges University 2 , Yichang 443002, China

Funding

National Natural Science Foundation of China · The Open Foundation of the Yichang Key Laboratory of Information Physics Fusion Defense and Control Syste

References (19)

Show source-provided references
1. Int J Appl Electron · 2017 · 55 · S133
2. IEEE Trans. Magn. · 1986 · 22 · 60310.1109/tmag.1986.1064347 ↗
3. IEEE Trans. Magn. · 2017 · 53 · 7301504
4. IEEE Trans. Magn. · 2018 · 54 · 7301405
5. J. Magn. Magn. Mater. · 2006 · 300 · 45110.1016/j.jmmm.2005.05.032 ↗
6. IEEE Trans. Magn. · 2005 · 41 · 234310.1109/tmag.2005.852177 ↗
7. IEEE Trans. Magn. · 2000 · 36 · 314110.1109/20.908713 ↗
8. J. Comput. Appl. Math. · 2016 · 406 · 258
9. IEEE Trans. Magn. · 2018 · 54 · 730020410.1109/tmag.2017.2748100 ↗
10. Physica B · 2006 · 372 · 6110.1016/j.physb.2005.10.020 ↗
11. Physica B · 2004 · 343 · 14210.1016/j.physb.2003.08.086 ↗
12. AIP Adv · 2021 · 11 · 01502210.1063/9.0000030 ↗
13. IEEE Trans. Magn. · 1999 · 35 · 124310.1109/20.767175 ↗
14. IEEE Trans. Magn. · 2008 · 44 · 86210.1109/tmag.2007.916484 ↗
15. IEEE Trans. Ind. Electron. · 2019 · 66 · 617010.1109/tie.2018.2873111 ↗
16. IEEE Trans. Magn. · 2006 · 42 · 196310.1109/tmag.2006.877463 ↗
17. AIP Adv · 2019 · 9 · 02511110.1063/1.5054948 ↗
18. IEEE Trans. Magn. · 2011 · 47 · 39510.1109/tmag.2010.2097274 ↗
19. IEEE Trans. Magn. · 2014 · 50 · 730090410.1109/tmag.2014.2329836 ↗