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DC-Link Current Minimization Control for Current Source Converter-Based Solid-State Transformer
DOI:10.1109/TPEL.2022.3178433.png)
Abstract
En 中文
This article proposes a fast predictive control method and a small dc-link inductor to minimize the dc-link current in current-source converter (CSC) based solid-state transformer. The dc-link current minimization can significantly reduce power loss and improve efficiency. The challenge of this problem is on improving both steady-state and dynamic performance. PI control methods and large dc-link inductors are conventionally used in the CSC but have limited dynamic performance. A model predictive control (MPC) method is proposed to achieve switching-cycle-level settling time, and the dc-link inductor is sized for 40% ripple to enable fast current change. Importantly, this article also proposes to minimize the dc-link current by varying the current even within a line cycle under single-phase load to improve the steady-state performance, in contrast with the reduction to a constant value in the literature. The proposed MPC features a constant switching frequency without weighting factors. The MPC does not have a high computational burden and is implemented in a regular digital controller for a prototype of soft-switching solid-state transformer (S4T) with reduced conduction loss. The effectiveness of the proposed method has been experimentally verified on the SiC S4T prototype during steady-state and dynamics under different multiport power flow conditions up to 2 kV peak. The dc-link current in the experiments is close to the minimum current with a short zero-vector duration, which further verifies the performance of the proposed method.
Keywords:
Transformers
Inductors
Minimization
Steady-state
Switches
Ports (computers)
Zero voltage switching
Current-source inverter (CSI)
efficiency improvement
loss reduction
medium voltage (MV)
medium-frequency transformer (MFT)
solid-state transformer (SST)
three-port power electronic transformer (PET)
variable dc link
Journal
IF:
6.5
Papers:
1.7W
Citations:
8.3W

