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A novel predictive control strategy for neutral-point clamped converter with harmonic spectrum shaping

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posted on 2021-05-23, 13:45 authored by Jaksa Rubinic
This thesis proposes a new predictive control strategy to achieve fixed-switching frequency operation for a neutral-point clamped (NPC) inverter. The classical fixed-sampling frequency finite control-set model predictive control (FSF-FCS-MPC) operates with variable switching frequency, and thus produces spread-spectrum in an output current. The classical method also suffers from high computational complexity as the number of converter voltage levels increases. To overcome these issues, a high performance variable sampling frequency finite control-set model predictive control (VSF-FCS-MPC) strategy is proposed to control the power converters. The proposed control technique combines the advantages of space vector modulation (SVM) with a newly introduced mechanics to determine the appropriate sampling frequency. With these features the major requirements such as balancing of DC-link capacitor voltages, switching frequency minimization and common-mode voltage mitigation have been achieved with simultaneous elimination of even-order and inter-harmonics in the load current harmonic spectrum. The VSF-FCS-MPC strategy for current control with decoupled active/reactive power regulation of grid-connected multilevel converter was also analyzed. Moreover, a novel DC-link voltage balancing technique is presented which eliminates the need for balancing the capacitor voltages through cost function, and thus alleviates the weighting factor design. An introduction of SVM highly reduces the calculation time by considering only adjacent vectors, rendering FCS-MPC more suitable for implementation with multi-level converters with a number of voltage levels higher than three. Finally, the proposed control technique has been validated through simulation and experimental verification and a detailed comparison of VSF-FCS-MPC with FSF-FCS-MPC and SVM is presented





Master of Science


Electrical and Computer Engineering

Granting Institution

Ryerson University

LAC Thesis Type