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Enhanced Fault-Tolerant Robust Deadbeat Predictive Control for Nine-Level ANPC-Based Converter

Research Authors
Ibrahim Harbi, Mostafa Ahmed, Marcelo Lobo Heldwein, Ralph Kennel, Mohamed Abdelrahem
Research Department
Research Date
Research Year
2022
Research Journal
IEEE Access
Research Publisher
IEEE
Research Vol
10
Research Rank
Q1
Research_Pages
108492-108505
Research Website
https://ieeexplore.ieee.org/document/9915390
Research Abstract

Deadbeat model predictive control (DB-MPC) is one of the advanced promising control methods for power converters thanks to its simplicity, high steady-state performance and fast dynamic response. However, the high sensitivity to parameter mismatch and the difficulty of handling multiple control targets are problematic issues in DB-MPC. This work presents an improved robust DB-MPC for a new nine-level ANPC-based inverter. This inverter requires a low number of power devices compared to other single dc-source inverters. Only nine active switches and two discrete diodes are utilized to obtain a nine-level waveform. Without the need for weighting factors, the proposed DB-MPC method tackles three control goals; current control, flying capacitors (FCs) stabilization and dc-link balance, which saves the laborious effort of adjusting the weighting factors in the traditional finite control set MPC (FCS-MPC) method. Moreover, an effective dc-link balancing scheme based on power flow control is proposed and integrated into the FCs control objective. To enhance the control robustness, an EKF-based estimator is designed to identify the system parameters online. In addition, the proposed DB-MPC scheme allows the considered inverter to continue operating with the generation of five levels in the failure condition of the four-quadrant switch, improving the fault tolerance of the inverter. The developed DB-MPC method is experimentally verified in steady-state and transient operation. To demonstrate the excellent performance of the presented DB-MPC scheme, experimental comparisons with other popular MPC methods are performed.

Research Rank
International Journal