Affiliation:
1. School of Electrical Engineering, Shandong University, Jinan 250061, China
2. State Grid Shandong Electric Power Research Institute, Jinan 250000, China
Abstract
Modular multilevel converters (MMCs) have gained widespread adoption in high-voltage direct current (HVDC) transmission due to their high voltage levels, low harmonic content, and high scalability. However, conventional control methods such as finite control set model predictive control (FCS-MPC) suffer from a heavy computational burden and sensitivity to system parameter variations, limiting the performance of MMCs. This paper proposes a data-driven approach based on model-free adaptive control with an event-triggered mechanism that demonstrates superior robustness against parameter mismatches and enhanced dynamic performance in response to sudden output changes. Moreover, the introduction of the event-triggered mechanism effectively reduces redundant operations, decreasing the computational burden and switching losses. Finally, the proposed strategy is validated through a MATLAB/Simulink simulation model.
Funder
General Program of the National Natural Science Foundation of China
Reference41 articles.
1. Modular Multilevel Converters for HVDC Applications: Review on Converter Cells and Functionalities;Nami;IEEE Trans. Power Electron.,2015
2. Martinez-Rodrigo, F., Ramirez, D., Rey-Boue, A.B., De Pablo, S., and Herrero-de Lucas, L.C. (2017). Modular multilevel converters: Control and applications. Energies, 10.
3. Model-Predictive Control of Multilevel Inverters: Challenges, Recent Advances, and Trends;Harbi;IEEE Trans. Power Electron.,2023
4. A Control Method of HVDC-Modular Multilevel Converter Based on Arm Current Under the Unbalanced Voltage Condition;Moon;IEEE Trans. Power Deliv.,2015
5. Arm-Current-Based Model Predictive Control for Modular Multilevel Converter Under Unbalanced Grid Conditions;Li;IEEE J. Emerg. Sel. Top. Power Electron.,2022