Journal of Power Electronics

  • 제18권6호
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  • Pages.1791-1804
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  • 2018
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  • 1598-2092(pISSN)
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  • 2093-4718(eISSN)
전력전자학회 (The Korean Institute of Power Electronics)
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Deadbeat and Hierarchical Predictive Control with Space-Vector Modulation for Three-Phase Five-Level Nested Neutral Point Piloted Converters

  • Li, Junjie (Key Laboratory of Control of Power Transmission and Conversion, Ministry of Education, Shanghai Jiao Tong University) ;
  • Chang, Xiangyu (School of Russian and Eurasian Studies, Shanghai International Studies University) ;
  • Yang, Dirui (Key Laboratory of Control of Power Transmission and Conversion, Ministry of Education, Shanghai Jiao Tong University) ;
  • Liu, Yunlong (School of Mechanical Electronic and Information Engineering, China University of Mining and Technology) ;
  • Jiang, Jianguo (Key Laboratory of Control of Power Transmission and Conversion, Ministry of Education, Shanghai Jiao Tong University)
  • 투고 : 2018.05.13
  • 심사 : 2018.08.29
  • 발행 : 2018.11.20
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초록

To achieve a fast dynamic response and to solve the multi-objective control problems of the output currents, capacitor voltages and system constraints, this paper proposes a deadbeat and hierarchical predictive control with space-vector modulation (DB-HPC-SVM) for five-level nested neutral point piloted (NNPP) converters. First, deadbeat control (DBC) is adopted to track the reference currents by calculating the deadbeat reference voltage vector (DB-RVV). After that, all of the candidate switching sequences that synthesize the DB-RVV are obtained by using the fast SVM principle. Furthermore, according to the redundancies of the switch combination and switching sequence, a hierarchical model predictive control (MPC) is presented to select the optimal switch combination (OSC) and optimal switching sequence (OSS). The proposed DB-HPC-SVM maintains the advantages of DBC and SVM, such as fast dynamic response, zero steady-state error and fixed switching frequency, and combines the characteristics of MPC, such as multi-objective control and simple inclusion of constraints. Finally, comparative simulation and experimental results of a five-level NNPP converter verify the correctness of the proposed DB-HPC-SVM.

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