Y
Yongjian Li
Researcher at Hebei University of Technology
Publications - 160
Citations - 1386
Yongjian Li is an academic researcher from Hebei University of Technology. The author has contributed to research in topics: Magnetic flux & Electrical steel. The author has an hindex of 18, co-authored 130 publications receiving 945 citations. Previous affiliations of Yongjian Li include University of Technology, Sydney.
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Proceedings ArticleDOI
Modeling and Finite Element Calculation of Core Loss in Anode Saturable Reactor Based on JA Dynamic Hysteresis Model
TL;DR: In this paper, the Jiles-Atherton(J-A) dynamic hysteresis model was used to investigate the single core loss of anode saturable reactor, the theoretical calculation and electromagnetic simulation are carried out with the actual surge current.
Proceedings ArticleDOI
Research of Harmonic Effects on Core Loss in Soft Magnetic Composite Materials Based on Three-Dimensional Magnetic Test System
TL;DR: In this paper, a 3D magnetic test system with frequency domain feedback control method was used for core loss measurement of soft magnetic composite materials (SMC) under both sinusoidal and non-sinusoidal excitations.
Journal ArticleDOI
Modeling and validation of stray-field loss inside magnetic and non-magnetic components under harmonics-DC hybrid excitations based on updated TEAM Problem 21
Zhiguang Cheng,Behzad Forghani,Du Zhenbin,Lanrong Liu,Yongjian Li,Xiaojun Zhao,Liu Tao,Cai Linfeng,Weiming Zhang,Lu Meilin,Yakun Tian,Yating Li +11 more
TL;DR: In this article, the authors proposed and established a set of new benchmark models to investigate and confidently validate the modeling and prediction of total stray-field loss inside magnetic and non-magnetic components under harmonics-direct current (HDC) hybrid excitations.
Journal ArticleDOI
Analysis of Magnetizing Process Using the Discharge Current of Capacitor by Method
TL;DR: In this paper, the effects of the eddy currents in the permanent magnet during the magnetizing process on residual flux distribution have been also analyzed, and the effectiveness of the T-Ω method used in the magnetization process of permanent magnets was certified by a comparison between the calculated average magnetization ratios and the measured ones.
Journal ArticleDOI
Parameters Calculation and Application of Preisach Hysteresis Model Based on Combination of Genetic Algorithms and Neural Networks
TL;DR: A modified method, combining Genetic Algorithm and Neural Network Method to get three conventional parameters of the Preisach hysteresis model under alternating magnetization is presented, found to be in good agreement with the measurement data and verify the theory.