Iron Loss Calculation of High Frequency Transformer Based on A Neural Network Dynamic Hysteresis Model
- Publisher:
- Institute of Electrical and Electronics Engineers (IEEE)
- Publication Type:
- Conference Proceeding
- Citation:
- CEFC 2022 - 20th Biennial IEEE Conference on Electromagnetic Field Computation, Proceedings, 2022, 00, pp. 1-2
- Issue Date:
- 2022-01-01
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
| Iron_Loss_Calculation_of_High_Frequency_Transformer_Based_on_A_Neural_Network_Dynamic_Hysteresis_Model.pdf | Published version | 255.19 kB |
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The soft magnetic core is a key element that determines the performance of electromagnetic devices, such as transformers, motors, reactors, etc. For large capacity high frequency transformers, it is essential to accurately predict the core losses at high frequencies in order to improve the efficiency and power density, as well as other performance through design optimisation. This paper presents a dynamic hysteresis model based on neural networks for calculating the iron losses in amorphous magnetic cores of high frequency transformers under non-sinusoidal magnetisations. The proposed dynamic hysteresis model has been incorporated into the finite element method to calculate the magnetic core loss distribution in high frequency transformer cores. The accuracy and effectiveness of the model has been validated by comparing the theoretical and experimental results.
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