Abstract
The operating condition of the traction transformer has an important impact on the safe and smooth operation of the traction power supply system. It should be noted that the temperature distribution can reflect the operating state of the transformer. Therefore, it is necessary to study the transformer temperature field. The numerical models commonly used at present to solve the temperature field are computationally costly in terms of time and difficult to derive the temperature in real-time. In order to solve this problem, the reduced-order modeling of transformers in coupled multi-physical domains is proposed in the paper. First, a full-order three-dimensional model of the traction transformer is established and the accuracy of the model is verified. Second, the reduced-order model is obtained by mode analysis based on the full-order model. Finally, the reduced-order model is applied to solve the temperature field, and the calculation error and time of different models are compared. The results show that the error between the calculated values of the reduced-order model and the full-order model is within 0.05 °C while the calculation time is improved by 3 times compared with the full-order model, which verifies the accuracy and timeliness of the reduced-order model.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Sun, Y., Hua, Y., Wang, E., et al.: A temperature-based fault pre-warning method for the dry-type transformer in the offshore oil platform. Int. J. Electr. Power Energy Syst. 123 (2020)
Liu, Y., Li, X., Li, H., et al.: Spatially continuous transformer online temperature monitoring based on distributed optical fibre sensing technology. High Voltage Eng. 7(2), 336–345 (2022)
Liu, D., Wang, Y., Zhang, Y., et al.: Temperature prediction of transformer high-voltage bushing based on PSO-LSTM, In: 2022 IEEE Conference on Electrical Insulation and Dielectric Phenomena (IEEE CEIDP 2022), pp. 91–94 (2022)
Doolgindachbaporn, A., Callender, G., Lewin, P., et al.: Data driven transformer thermal model for condition monitoring. IEEE Trans Power Delivery 37(4), 3133–3141 (2022)
Ma, X., Liu, Y., Li, C., et al.: Battery SOC estimation method based on BP neural network optimized by genetic algorithm. Lecture Notes in Electrical Engineering, vol. 868 LNEE, pp. 45–56 (2022). https://doi.org/10.1007/978-981-16-9913-9_6
Kaminski, A.M., Medeiros, L.H., Bender, V.C., et al.: Artificial neural networks application for top oil temperature and loss of life prediction in power transformers. Electric Power Components Syst. 50(11–12), 549–560 (2022)
Yuan, F.T., Wang, Y., Tang, B., et al.: Heat dissipation performance analysis and structural parameter optimization of oil-immersed transformer based on flow-thermal coupling finite element method. Thermal Sci. 26(4), 3241–3253 (2022)
Alonso, P.E.B., Meana-Fernández, A., Fernández Oro, J.M.: Thermal response and failure mode evaluation of a dry-type transformer. Appl. Thermal Eng. 120 (2017)
Pierquin, A., Henneron, T.: Nonlinear data-driven model order reduction applied to circuit-field magnetic problem. IEEE Trans Magnetics 57(11) (2021)
Liu, G., Rong, S., Wu, W., et al.: Two-dimensional transient flow-thermal coupling field analysis of oil-immersed transformer windings based on hybrid finite element method and reduced-order technology. High Voltage Eng. 48(5), 1695–1705 (2022) (in Chinese)
**g, L., Dong, X., Yang, C., et al.: Research on finite element reduced order modeling method of transformer temperature field for digital twin application1. High Voltage Eng. 1–12 (2022). https://doi.org/10.13336/j.1003-6520.hve.20220689 (in Chinese)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 Bei**g Paike Culture Commu. Co., Ltd.
About this paper
Cite this paper
Liu, Y., Zhang, G., Yang, J. (2024). Reduced-Order Modeling of Traction Transformer Considering Coupling of Electric-Thermal-Fluid Multi-physical Domains. In: Yang, J., Yao, D., Jia, L., Qin, Y., Liu, Z., Diao, L. (eds) Proceedings of the 6th International Conference on Electrical Engineering and Information Technologies for Rail Transportation (EITRT) 2023. EITRT 2023. Lecture Notes in Electrical Engineering, vol 1136. Springer, Singapore. https://doi.org/10.1007/978-981-99-9315-4_28
Download citation
DOI: https://doi.org/10.1007/978-981-99-9315-4_28
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-9314-7
Online ISBN: 978-981-99-9315-4
eBook Packages: EngineeringEngineering (R0)