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Parameter Estimation of DC Black-Box Arc Models Using System Identification Theory

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Abstract

This paper presents a demonstration of the system identification theory applied to the estimation of Cassie and Mayr arc models’ parameters. The purpose of this work is to present a process that allows to estimate the arc model parameters and verify its performances in the representation of a DC short-circuit in a railway system protected by a high-speed circuit breaker. The results show that the arc models can describe the relationship between the arc and the electrical circuit with good precision, but their generalization capacity is limited.

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Acknowledgements

The authors are grateful to Artur Rojek and Marek Skrzyniarz for their work with DC circuit breakers applied to railway system, which served as basis and provided the reference data for this work. An early version of paper was presented at XXIII Congresso Brasileiro de Automática (CBA 2020).

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  1. School of Electrical and Computer Engineering, University of Campinas, Campinas, Brazil

    Felipe P. Pessoa, Jhair S. Acosta & Maria C. Tavares

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  1. Felipe P. Pessoa
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  2. Jhair S. Acosta
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  3. Maria C. Tavares
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The authors acknowledge the financial support provided by the Brazilian institutions CAPES (code 001), and CNPq under Grants 307237/2020-6 and 166318/2020-5.

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Pessoa, F.P., Acosta, J.S. & Tavares, M.C. Parameter Estimation of DC Black-Box Arc Models Using System Identification Theory. J Control Autom Electr Syst 33, 1229–1236 (2022). https://doi.org/10.1007/s40313-021-00875-x

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  • DOI: https://doi.org/10.1007/s40313-021-00875-x

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