Model predictive control for Vienna rectifier with constant frequency based on inductance parameters online identification

Dang, ChaoLiang; Wang, Yihua; Jiang, ZeHao; Liu, Ding; Tong, XiangQian; Wheeler, Pat; Xin, Yechun

Abstract

When the finite control set model predictive control (FCS-MPC) is applied to the three-level converter, there are problems such as large current harmonics, high requirements for the computing efficiency of the microcontroller, complex multi-objective optimization and limited output vector switching. In addition,the mismatch of inductance parameter may directly affect the observation accuracy of FCS-MPC. To solve the above problem,a double vector model predictive control strategy with constant frequency strategy based on the inductance parameters on-line identification is presented.First, a single objective cost function based on the direct power is constructed by optimizing the redundant vector which is selected to balance the neutral-point potential, the design of weighting factor is avoided.At the same time, in order to effectively reduce the grid current ripple under single vector modulation, space vector pulse width modulation (SVPWM) is realized by combining with zero vector control mode. And, a parameter identification method based on model reference adaptive system(MARS) is proposed to overcome the effect of model parameter mismatch.Finally, the results show that the proposed F-MPCCF has good steady-state and dynamic performance from the static, transient and neutral-point potential control.

Citation

Dang, C., Wang, Y., Jiang, Z., Liu, D., Tong, X., Wheeler, P., & Xin, Y. (2024). Model predictive control for Vienna rectifier with constant frequency based on inductance parameters online identification. Journal of The Franklin Institute, 361(7), Article 106795. https://doi.org/10.1016/j.jfranklin.2024.106795

• Journal Article Type: Article
• Acceptance Date: Mar 23, 2024
• Online Publication Date: Mar 29, 2024
• Publication Date: 2024-05
• Deposit Date: Jun 3, 2024
• Publicly Available Date: Mar 30, 2025
• Journal: Journal of the Franklin Institute
• Print ISSN: 0016-0032
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 361
• Issue: 7
• Article Number: 106795
• DOI: https://doi.org/10.1016/j.jfranklin.2024.106795
• Public URL: https://nottingham-repository.worktribe.com/output/33567445
• Publisher URL: https://www.sciencedirect.com/science/article/pii/S0016003224002163?via%3Dihub