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Issue title: Deep learning for analysis and synthesis in electromagnetics
Guest editors: Maria Evelina Mognaschi
Article type: Research Article
Authors: Ma, Yangyanga; b; c | Li, Yongjiana; | Chen, Ruiyinga; b | Yue, Shuaichaob | Sun, Hea; b
Affiliations: [a] State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin, China | [b] Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability of Hebei Province, Hebei University of Technology, Tianjin, China | [c] State Grid Cangzhou Electric Power Supply Company, Cangzhou, China
Correspondence: [*] Corresponding author: Yongjian Li, School of Electrical Engineering, Hebei University of Technology, Tianjin, China. Tel.: +86 13802065108; E-mail: liyongjian@hebut.edu.cn
Abstract: With the increase in power electronic equipment in power system, the excitation of ferromagnetic materials often involves a large number of harmonics. Therefore, it is necessary to construct an accurate dynamic hysteresis model to adapt to this complicated operating state of electrical equipment. In this paper, a Hybrid Dynamic Hysteresis Model (HDHM), which can effectively characterize the harmonic excitation of materials is studied based on the Preisach model and Stacked Auto-Encoder (SAE) model. The static part of this model takes the form of the inverse Preisach model. And the Multiple Dynamic Hysteresis Model Set (MDHMS) is constructed by multiple dynamic models of eddy currents and excess characteristics of the ferromagnetic materials. The dynamic part of the HDHM takes the form of the model structure combining the Stacked Auto-encoder and the MDHMS. The calculation results of the hysteresis loop and ferromagnetic loss in the harmonic condition of silicon steel sheet proves the validity of this model. Moreover, compared with the conventional dynamic hysteresis model, the HDHM has better accuracy and generalization ability.
Keywords: Hybrid dynamic hysteresis model, the Preisach model, multiple-model set theory, ferromagnetic materials, iron loss
DOI: 10.3233/JAE-220112
Journal: International Journal of Applied Electromagnetics and Mechanics, vol. 73, no. 4, pp. 399-413, 2023
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