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Leakage Current Suppression of Transformerless 5L-ANPC Inverter With Lower Ripple Model Predictive Control.
- Source :
-
IEEE Transactions on Industry Applications . Sep/Oct2022, Vol. 58 Issue 5, p6297-6309. 13p. - Publication Year :
- 2022
-
Abstract
- The five-level active neutral-point-clamped (5L-ANPC) inverter has been applied to increase the power capacity of photovoltaic (PV) systems. However, this type of configuration requires to balance neutral point (NP) voltage and flying capacitor (FC) voltage properly. For the operation of the transformerless five-level inverter, another requirement is to suppress the leakage current associated with the common-mode voltage (CMV) vectors. Due to the mutually coupled issues, the conventional model predictive control (MPC) methods reported so far cannot deal with them properly at the same time. To address these problems, a novel double-vector MPC (DV-MPC) strategy with zero CMV vector is proposed in this article. First, 19 voltage vectors with zero CMV are implemented as candidate vectors. Then, for the purpose to reduce output current ripples, two voltage vectors are penalized by the cost function without weighting factors to track the reference voltage. In addition, the dwell times are optimized to further mitigate current ripples. Finally, to avoid the sharply increasing computational burden of the online iteration caused by the introduction of the second vector and its corresponding redundant switching states, a voltage-balance strategy is presented for the DV-MPC method, which can determine the suitable switching combinations directly and adapt them to the selected vectors to balance the voltages of the NP and FCs together. The effectiveness of the proposed method is verified by the simulation and experiment at the end of this article. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 00939994
- Volume :
- 58
- Issue :
- 5
- Database :
- Academic Search Index
- Journal :
- IEEE Transactions on Industry Applications
- Publication Type :
- Academic Journal
- Accession number :
- 160651520
- Full Text :
- https://doi.org/10.1109/TIA.2022.3178818