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FPGA-based real-time simulation of LCC-HVDC systems with C-NAM method

  • Ruolan Li (Department of Electronic Information Engineering, Beijing Jiaotong University) ;
  • Danyong Li (Department of Electronic Information Engineering, Beijing Jiaotong University) ;
  • Yang Gao (State Key Laboratory of Advanced Transmission Technology, State Grid Smart Grid Research Institute Co., Ltd.) ;
  • Chen Gu (State Grid Jiangsu Electric Power Co., Ltd., Yangzhou Power Supply Branch) ;
  • Xiaoyi Sun (Department of Electronic Information Engineering, Beijing Jiaotong University) ;
  • Li Fan (Department of Electronic Information Engineering, Beijing Jiaotong University)
  • Received : 2022.08.05
  • Accepted : 2022.12.23
  • Published : 2023.06.20

Abstract

The real-time simulation of a line-commutated converter-high voltage direct current (LCC-HVDC) system based on a field programmable gate array (FPGA) is proposed to meet the increasingly complex dynamic characteristics and real-time simulation requirements of HVDCs. In addition, a real-time simulation platform based on a CPU+FPGA is established for simulation verification. First, a simulation model of the 12-pulse rectifier of an LCC-HVDC is established using the compact nodal analysis method (C-NAM). When compared with the traditional node analysis method, C-NAM reduces the number of multiplication executions in a single simulation step, and the degree of program serialization is significantly reduced, which greatly improves the real-time simulation speed of a FPGA. Then, this simulation model is programmed in a FPGA, and the optimization algorithm further shortens the simulation step size. Finally, a 12-pulse LCC-HVDC is compared and verified with a 1 µs simulation step on a FPGA real-time simulation platform, and a simulation analysis verifies the accuracy of the model. This method can improve the simulation scale of an LCC-HVDC system, and enhance the versatility of the LCC-HVDC real-time simulator based on a FPGA.

Keywords

Acknowledgement

This work was supported by the Fundamental Research Funds for the Central Universities under Grant 2020JBM011.

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