Abstract
This article investigates a data-driven-based predictive current control (DD-PCC) approach for a modular multilevel converter (MMC) to circumvent the sensitiveness to parameter variation and unmodeled dynamics of a finite control-set model predictive control (FCS-MPC) method. By integrating a model-free adaptive control (MFAC)-based data-driven solution into the FCS-MPC framework, the performance deterioration caused by model uncertainties is suppressed. The design of the suggested controller is only based on input-output measurement data, where neither the parameter information nor the knowledge of detailed MMC models is required, leading to improved robustness against parameter drifts and model uncertainness. Moreover, a simplified cost function formula that takes into account output current tracking and circulating current regulation is constructed to efficiently determine the optimal insertion index of each arm. Finally, simulation and experimental results are obtained to verify the steady-state, dynamics, and robustness performance of the proposed approach.
| Original language | English |
|---|---|
| Pages (from-to) | 523-531 |
| Number of pages | 9 |
| Journal | IEEE Journal of Emerging and Selected Topics in Power Electronics |
| Volume | 11 |
| Issue number | 1 |
| DOIs | |
| State | Published - 2023 |
Bibliographical note
Funding Information:This work was supported in part by the National Natural Science Foundation of China under Grant 51807177 and Grant 51827810, in part by the China Postdoctoral Science Foundation under Grant 2020M681855, and in part by the Natural Science Foundation of Zhejiang Province under Grant LY21E070004 and Grant LY22E070003. The work of Jose Rodriguez was supported by the Agencia Nacional de Investigacion y Desarrollo (ANID) under Project FB0008, Project 1210208, and Project 1221293.
Publisher Copyright:
© 2013 IEEE.
ASJC Scopus subject areas
- Energy Engineering and Power Technology
- Electrical and Electronic Engineering