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A Deep-Neural-Network-Based Surrogate Model for DC/AC Converter Topology Selection Using Multi-Domain Simulations

Author

Listed:
  • Gabriel Avila Saccol

    (Department of Electrical and Computer Engineering, University of Michigan-Dearborn, Dearborn, MI 48128, USA)

  • Van-Hai Bui

    (Department of Electrical and Computer Engineering, University of Michigan-Dearborn, Dearborn, MI 48128, USA)

  • Wencong Su

    (Department of Electrical and Computer Engineering, University of Michigan-Dearborn, Dearborn, MI 48128, USA)

Abstract

The selection of optimal DC/AC power converter topologies for specific applications is often a time-consuming and complex task, which can lead to suboptimal choices. This paper proposes an AI-assisted methodology to identify the most efficient DC/AC converter based on a set of input design parameters. Separate deep-neural-network-based surrogate models are developed for each considered topology, trained by a large dataset of simulation results obtained from MATLAB/Simulink and PSIM, so that the efficiency of each converter can be determined without performing additional simulations. The proposed methodology allows for quick and accurate efficiency estimation, significantly reducing the analysis time for topology selection. A case study for the two-level converter is also presented, demonstrating that additional parameters, such as the semiconductors junction temperature and output current distortion, can also be predicted using a similar methodology. Results are presented to demonstrate the feasibility of the proposed method.

Suggested Citation

  • Gabriel Avila Saccol & Van-Hai Bui & Wencong Su, 2024. "A Deep-Neural-Network-Based Surrogate Model for DC/AC Converter Topology Selection Using Multi-Domain Simulations," Energies, MDPI, vol. 17(24), pages 1-19, December.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:24:p:6467-:d:1549974
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    References listed on IDEAS

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    1. Abdul Mannan Rauf & Mohamed Abdel-Monem & Thomas Geury & Omar Hegazy, 2023. "A Review on Multilevel Converters for Efficient Integration of Battery Systems in Stationary Applications," Energies, MDPI, vol. 16(10), pages 1-38, May.
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    4. Subhashree Choudhury & Mohit Bajaj & Taraprasanna Dash & Salah Kamel & Francisco Jurado, 2021. "Multilevel Inverter: A Survey on Classical and Advanced Topologies, Control Schemes, Applications to Power System and Future Prospects," Energies, MDPI, vol. 14(18), pages 1-48, September.
    5. Ali Abdel-Aziz & Mohamed A. Elgenedy & Barry Williams, 2023. "A Comparative Review of Three Different Power Inverters for DC–AC Applications," Energies, MDPI, vol. 16(21), pages 1-21, October.
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