Temperature-dependent hysteresis model based on temporal convolutional network
Source abstract
Abstract
The variation of temperature modifies the magnetic behavior of ferromagnetic cores which may affect the performance of electrical equipment. Therefore, it is imperative to develop a temperature-dependent hysteresis model to precisely calculate electromagnetic characteristics of electrical equipment. In this paper, a Temporal Convolutional Network (TCN) in combination with the Play operator is developed. The proposed model incorporates the temperature-dependent spontaneous magnetization intensity as the model input to introduce the temperature effect. To enhance the accuracy of model training outcomes, the Bayesian optimization approach for automatically selecting network model parameters is provided. The results show that the proposed model can accurately predict the hysteresis characteristics of materials under varying temperature and frequency conditions.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Magnetic Properties and Applications · Non-Destructive Testing Techniques · Electric Motor Design and Analysis
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
State Key Laboratory of EERI, School of Electrical Engineering, Hebei University of Technology 1 , Tianjin 300130, China
Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability of Hebei Province, Hebei University of Technology 2 , Tianjin 300130, China
Funding
The National Natural Science Foundation of China · The National Nature Science Foundation of China · Natural Science Foundation of Hebei Province
References (13)
Show source-provided references
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