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A Two-Step Continuous-Control-Set MPC for Modular Multilevel Converters Operating With Variable Output Voltage and Frequency

  • Yeiner Arias-Esquivel
  • , Roberto Cardenas
  • , Luca Tarisciotti
  • , Matias Diaz
  • , Andres Mora

Research output: Contribution to journalArticlepeer-review

22 Scopus citations

Abstract

This article presents a new enhanced single-stage two-step continuous control set model predictive control (CCS-MPC) algorithm, for modular multilevel converters (MMCs) operating with variable output frequency at the ac port. The proposed two-step CCS-MPC allows the implementation of feed-forward compensation of the one-step ahead perturbations at the ac side, using an algorithm based on the Moore-Penrose pseudoinverse matrix. Limitations of the current and voltages in the MMC clusters are implemented to avoid overmodulation and overcurrents, being the solution of the constrained CCS-MPC online solved using the active-set method. Without losing generality, the experimental validation of the single-stage CCS-MPC is performed using an 18-cell MMC prototype, driving a 7.5-kW cage induction machine operating in a wide speed range. A dSPACE MicroLabBox platform is used to control the MMC-based drive by implementing the CCS-MPC algorithm, the modulation stage and the active-set method. It is experimentally demonstrated that the compensation of the low-frequency perturbations allows a considerable reduction in the required circulating currents at the low-frequency operation of the drive. Reduction in the capacitor voltage ripple, improved efficiency, and a smaller total harmonic distortion in the output currents are also achieved with the proposed single-stage two-step CCS-MPC.

Original languageEnglish
Pages (from-to)12091-12103
Number of pages13
JournalIEEE Transactions on Power Electronics
Volume38
Issue number10
DOIs
StatePublished - 1 Oct 2023

Keywords

  • Circulating current
  • Moore-Penrose pseudoinverse
  • model predictive control
  • modular multilevel converter (MMC)
  • voltage balancing
  • weighting factors

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