1. Power Transformer Model in Railway Applications Based on Bond Graph and Parameter Identification
- Author
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Saba Amirdehi, Paul-Etienne Vidal, Johana Vally, Baptiste Trajin, Didier Colin, Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE), ALSTOM Transport (FRANCE), Laboratoire Génie de Production - LGP (Tarbes, France), Alstom Transport SA, ALSTOM, Laboratoire Génie de Production (LGP), and Ecole Nationale d'Ingénieurs de Tarbes
- Subjects
Test bench ,Computer science ,Energy Engineering and Power Technology ,Transportation ,01 natural sciences ,Power transformer simulation ,law.invention ,System model ,law ,Control theory ,0103 physical sciences ,Electronique ,Electrical and Electronic Engineering ,Transformer ,Parameters identification ,Hysteresis cycle ,010302 applied physics ,Estimation theory ,010401 analytical chemistry ,Traction chain ,Experimental data ,Modélisation et simulation ,[INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation ,Magnetic flux ,[SPI.TRON]Engineering Sciences [physics]/Electronics ,0104 chemical sciences ,Nonlinear system ,Automotive Engineering ,Bond graph - Abstract
International audience; Validation and verification are the most important issues in railway applications due to cost and security reasons. Therefore, having a model of the system would be necessary in this case. Due to non-ideal test conditions in industrial applications, an accurate parameter identification process has to be defined. In this paper, bond graph method is used to model energy exchanges within components of a traction chain. More precisely, the non-linear transformer model and its parameter identification is studied. In the case of non-ideal test conditions, the usual Jiles-Atherton parameter identification procedure can not be performed. Regarding state of the art, the Jiles-Atherton parameter identification is discussed. It is highlighted that an uncomplete hysteresis cycle, including extremum point and coercive field are mandatory for an accurate parameter identification. The proposed identification process is applied to a real application case. The obtained parameters are then inserted into the overall system model. The consecutive simulations are compared to experimental data obtained through traction chain test bench.
- Published
- 2020