A Low‐Complexity Model Predictive Control Strategy With Optimized Switching Sequences to Enhance Voltage Performance for the Reduced Switch Count Three‐Level Inverter
ABSTRACT The conventional finite control set (FCS) model predictive control (MPC) strategy employed in the reduced switch count three‐level inverter (RSC‐TLI) utilizing an LC ‐filter exhibits significant output voltage ripple. However, conventional FCS faces the challenges of substantial computational complexity and nonconstant switching frequency. To mitigate the above issues, a low‐complexity MPC strategy incorporating optimized switching sequences is developed for the RSC‐TLI. First, six virtual medium vectors are synthesized to mitigate output voltage ripple for the first time. At the same time, three duty‐optimized vectors are introduced to maintain the reduced voltage ripple levels. Secondly, a boundary condition–based subsector optimization approach is introduced to streamline the implementation process. Meanwhile, a nonorthogonal coordinate system is introduced to eliminate the complex and numerous calculations caused by the intensive trigonometric operations. Then, the optimized switching sequences (OSSs) are proposed to ensure fixed switching frequency. Finally, hardware‐in‐loop (HIL) experiments are used to conduct the comprehensive experiments to validate the feasibility of the proposed strategy.
Authors
- Changwei Qin (ORCID: https://orcid.org/0000-0003-4919-4662)
- Xiaofeng Li (ORCID: https://orcid.org/0000-0002-2016-290X)
- Dongjiang Yang (ORCID: https://orcid.org/0000-0002-8745-9249)
- Lei Yang
- Yanling Zhang
Institutions
- China Ocean Shipping (China) (CN)
- Shandong Provincial Key Laboratory of Renewable Energy Building Application Technology (CN)
- Shandong Jianzhu University (CN)
Publication Details
- Journal
- International Journal of Circuit Theory and Applications
- Published
- 2026-04-01
- DOI
- https://doi.org/10.1002/cta.70403
- Primary Topic
- Multilevel Inverters and Converters
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China
- Natural Science Foundation of Shandong Province