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Cascaded Smart Gate Drivers for Modular Multilevel Converters Control: A Decentralized Voltage Balancing Algorithm

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  • Corentin Darbas

    (IETR UMR CNRS 6164, Université de Rennes 1, Campus Beaulieu, 263 ave. Général Leclerc, CEDEX, 35042 Rennes, France
    IREENA Université de Nantes, (CRTT), B.P. 406, 37 Bd de l’Université, CEDEX, 44602 Saint-Nazaire, France)

  • Jean-Christophe Olivier

    (IREENA Université de Nantes, (CRTT), B.P. 406, 37 Bd de l’Université, CEDEX, 44602 Saint-Nazaire, France)

  • Nicolas Ginot

    (IETR UMR CNRS 6164, Université de Rennes 1, Campus Beaulieu, 263 ave. Général Leclerc, CEDEX, 35042 Rennes, France)

  • Frédéric Poitiers

    (IETR UMR CNRS 6164, Université de Rennes 1, Campus Beaulieu, 263 ave. Général Leclerc, CEDEX, 35042 Rennes, France)

  • Christophe Batard

    (IETR UMR CNRS 6164, Université de Rennes 1, Campus Beaulieu, 263 ave. Général Leclerc, CEDEX, 35042 Rennes, France)

Abstract

Recent Modular Multilevel Converter (MMC) topology allows for drastic improvements in power electronic conversion such as higher energy quality, lower power semiconductors electrical stress, decreased Electro-Magnetic Interferences (EMI), and reduced switching losses. MMC is widely used in High Voltage Direct-Current (HVDC) transmissions as it offers, theoretically, no voltage limit. However, its control electronic structure is not modular itself. Especially, the insulation voltage between the submodule gate drivers’ primaries and secondaries depends on the number of submodules. The converter voltage levels cannot be increased without designing all gate driver isolations again. To solve that issue, the novel concept of distributed galvanic insulation is introduced for multilevel converters. The submodule’s gate drivers are daisy-chained, which naturally reduces the insulation voltage to the submodule capacitor voltage, regardless of the number of submodules. The MMC becomes truly modular as the number of submodules can be increased without impacting on the previous control electronic circuit. Such an innovative control structure weakens the link between the main control unit and the gate drivers. This inherent structural problem can be solved through the use of Smart-Gate Drivers (SGD), as they are often equipped with fast and bidirectional communication channels, while highly increasing the converter reliability. The innovation proposed in that work is the involvement of smart gate drivers in the distributed galvanic insulation-based MMC control and monitoring. First, the numerous benefits of smart gate drivers are discussed. Then, an innovative Voltage Balancing Algorithm directly integrated on the chained gate drivers is proposed and detailed. It features a tunable parameter, offering a trade-off between accurate voltage balancing and execution time. The proposed embedded algorithm features a low execution time due to simultaneous voltage comparisons. Such an algorithm is executed by the gate drivers themselves, relieving the main control unit in an original decentralized control scheme. A simulation model of a multi-megawatts three-phase grid-tied MMC inverter is realized, allowing validation of the proposed algorithm. Matlab/Simulink logic blocs allow us to simulate a typical CPLD/FPGA component, often embedded on smart gate drivers. The converter with the proposed embedded algorithm is simulated in steady-state and during load impact. The controlled delay and slew rate inferred by the algorithm do not disturb the converter behavior, allowing its conceptual validation.

Suggested Citation

  • Corentin Darbas & Jean-Christophe Olivier & Nicolas Ginot & Frédéric Poitiers & Christophe Batard, 2021. "Cascaded Smart Gate Drivers for Modular Multilevel Converters Control: A Decentralized Voltage Balancing Algorithm," Energies, MDPI, vol. 14(12), pages 1-27, June.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:12:p:3589-:d:576173
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    References listed on IDEAS

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    1. Ming Liu & Zetao Li & Xiaoliu Yang, 2020. "A Universal Mathematical Model of Modular Multilevel Converter with Half-Bridge," Energies, MDPI, vol. 13(17), pages 1-18, August.
    2. Fernando Martinez-Rodrigo & Dionisio Ramirez & Alexis B. Rey-Boue & Santiago De Pablo & Luis Carlos Herrero-de Lucas, 2017. "Modular Multilevel Converters: Control and Applications," Energies, MDPI, vol. 10(11), pages 1-26, October.
    3. Ming Liu & Zetao Li & Xiaoliu Yang, 2020. "Tracking Control of Modular Multilevel Converter Based on Linear Matrix Inequality without Coordinate Transformation," Energies, MDPI, vol. 13(8), pages 1-15, April.
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    2. Rodolfo Araneo & Salvatore Celozzi & Stefano Lauria & Erika Stracqualursi & Gianfranco Di Lorenzo & Marco Graziani, 2022. "Recent Trends in Power Systems Modeling and Analysis," Energies, MDPI, vol. 15(23), pages 1-7, December.

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