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

• Autorzy: Li Yanyan , Xiao Han , Jin Nan , Yan Guanglu

Identyfikatory

DOI

10.24425/aee.2022.140716

• Języki publikacji: EN

Abstrakty

EN

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 show that 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%.

Słowa kluczowe

EN

current reconstruction; current sensor fault; fault-tolerant; model predictive control; neutral point clamped three-level converter

• Wydawca: Polish Academy of Sciences, Committee on Electrical Engineering
• Czasopismo: Archives of Electrical Engineering
• Rocznik: 2022
• Tom: Vol. 71, nr 2
• Strony: 363--377
• Opis fizyczny: Bibliogr. 23 poz., rys., tab.

Twórcy

autor

Li Yanyan

• College of Electrical and Information Engineering, Zhengzhou University of Light Industry China

• https://orcid.org/0000-0001-6646-1393

autor

Xiao Han

• College of Electrical and Information Engineering, Zhengzhou University of Light Industry China

autor

Jin Nan

• College of Electrical and Information Engineering, Zhengzhou University of Light Industry China

• https://orcid.org/0000-0001-9763-711X

autor

Yan Guanglu

• College of Electrical and Information Engineering, Zhengzhou University of Light Industry China
• Nanyang Cigarette Factory, China Tobacco Henan Industrial Co., Ltd. China

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).