Model predictive control of NPC three-level grid-tied converter based on reconstructed current

The neutral point clamped (NPC) three-level grid-tied converter is the key equipment connecting renewable energy and power grids. The current sensor fault caused by harsh environment may lead to the split of renewable energy. The existing sensor fault-tolerant methods will reduce the modulation rati...

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Bibliographic Details
Main Authors: Yanyan Li, Han Xiao, Nan Jin, Guanglu Yang
Format: Article
Language:English
Published: Polish Academy of Sciences 2022-05-01
Series:Archives of Electrical Engineering
Subjects:
Online Access:https://journals.pan.pl/Content/123210/PDF/art06_internet.pdf
Description
Summary:The neutral point clamped (NPC) three-level grid-tied converter is the key equipment connecting renewable energy and power grids. The current sensor fault caused by harsh environment may lead to the split of renewable energy. The existing sensor fault-tolerant methods will reduce the modulation ratio index of the converter system. To ensure continuous operation of the converter system and improve the modulation index, a model predictive control method based on reconstructed current is proposed in this paper. According to the relationship between fault phase current and a voltage vector, the original voltage vector is combined and classified. To maintain the stable operation of the converter and improve the utilization rate of DC voltage, two kinds of fault phase current are reconstructed with DC current, normal phase current and predicted current, respectively. Based on reconstructed three-phase current, a current predictive control model is designed, and a model predictive control method is proposed. The proposed method selects the optimal voltage vector with the cost function and reduces time delay with the current reconstruction sector. The simulation and experimental results showthat the proposed strategy can keep the NPC converter running stably with one AC sensor, and the modulation index is increased from 57.7% to 100%.
ISSN:2300-2506