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Long-Horizon Nonlinear Model Predictive Control of Modular Multilevel Converters

Author

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  • Victor Daniel Reyes Dreke

    (Control System Group, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands
    These authors contributed equally to this work.)

  • Mircea Lazar

    (Control System Group, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands
    These authors contributed equally to this work.)

Abstract

Modular Multilevel Converters (MMCs) are a topology that can scale several voltage levels to obtain higher efficiency and lower harmonics than most voltage-source converters. MMCs are very attractive for renewable energy applications and fast charging stations for electric vehicles, where they can improve performance and reduce costs. However, due to the complex architecture and the large number of submodules, the current control of modular multilevel converters is a challenging task. The standard solution in practice relies on hierarchical decoupling and single-input-single-output control loops, which are limited in performance. Linearization-based model predictive control was already proposed for current control in MMCs, as it can optimize transient response and better handle constraints. In this paper, we show that the validity of linear MMC models significantly limits the prediction horizon length, and we propose a nonlinear MPC (NMPC) solution for current control in MMCs to solve this issue. With NMPC, we can employ long prediction horizons up to 100 compared to a horizon of 10, which is the limit for the prediction range of a linear MMC model. Additionally, we propose an alternative MMC prediction model and corresponding cost function, which enables directly controlling the circulating current and improves the capacitor voltages’ behavior. Using the state-of-the-art in sequential quadratic programming for NMPC, we show that the developed NMPC algorithm can meet the real-time constraints of MMCs. A performance comparison with a time-varying linearization-based MPC for an MMC topology used in ultra-fast charging stations for electric vehicles illustrates the benefits of the developed approach.

Suggested Citation

  • Victor Daniel Reyes Dreke & Mircea Lazar, 2022. "Long-Horizon Nonlinear Model Predictive Control of Modular Multilevel Converters," Energies, MDPI, vol. 15(4), pages 1-22, February.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:4:p:1376-:d:749012
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    References listed on IDEAS

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    1. Ajay Shetgaonkar & Aleksandra Lekić & José Luis Rueda Torres & Peter Palensky, 2021. "Microsecond Enhanced Indirect Model Predictive Control for Dynamic Power Management in MMC Units," Energies, MDPI, vol. 14(11), pages 1-26, June.
    2. Milovan Majstorovic & Marco Rivera & Leposava Ristic & Patrick Wheeler, 2021. "Comparative Study of Classical and MPC Control for Single-Phase MMC Based on V-HIL Simulations," Energies, MDPI, vol. 14(11), pages 1-17, May.
    3. Deepa Sankar & Lakshmi Syamala & Babu Chembathu Ayyappan & Mathew Kallarackal, 2021. "FPGA-Based Cost-Effective and Resource Optimized Solution of Predictive Direct Current Control for Power Converters," Energies, MDPI, vol. 14(22), pages 1-26, November.
    4. 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.
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    Cited by:

    1. Mustafa Gokdag, 2022. "Modulated Predictive Control to Improve the Steady-State Performance of NSI-Based Electrification Systems," Energies, MDPI, vol. 15(6), pages 1-19, March.

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