LAUSR.org creates dashboard-style pages of related content for over 1.5 million academic articles. Sign Up to like articles & get recommendations!

Simplified Indirect Model Predictive Control Method for a Modular Multilevel Converter

Photo from wikipedia

Demand for modular multilevel converters (MMCs) has been steadily increasing for utilization in medium- to high-power applications because of qualities such as high modularity, easy scalability, and superior harmonic performance.… Click to show full abstract

Demand for modular multilevel converters (MMCs) has been steadily increasing for utilization in medium- to high-power applications because of qualities such as high modularity, easy scalability, and superior harmonic performance. Furthermore, there has been a growing trend toward utilizing model predictive control for MMCs due to its simplicity, good dynamic response, and ease of multi-objective control. However, the rise in computational load leads to a great drawback when increasing the number of submodules (SMs). This paper presents an approach to reducing the computational load and using on-state SMs and circulating currents, by preselecting the number of SMs inserted in the upper and lower arms. This approach is based on using the number of on-state SMs and the circulating current, to compute the number of SMs inserted in the upper and lower arms, which is evaluated in the next sampling instant. This facilitates a significant reduction in the number of control options and the computational load. A sorting algorithm is used to retain the balancing capacitor voltages in each SM, while the cost function guarantees the regulation of the ac-side currents, arm voltages, and MMC circulating currents. Simulation and experiment results validate the performance of the proposed approach.

Keywords: model predictive; modular multilevel; control; number; predictive control

Journal Title: IEEE Access
Year Published: 2018

Link to full text (if available)


Share on Social Media:                               Sign Up to like & get
recommendations!

Related content

More Information              News              Social Media              Video              Recommended



                Click one of the above tabs to view related content.