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1.智能电网教育部重点实验室(天津大学),天津市 300072
2.直流输电技术国家重点实验室(中国南方电网科学研究院有限责任公司),广东省广州市 510663
Received:31 October 2022,
Revised:2023-02-07,
Published Online:25 April 2023,
Published:10 June 2023
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付浩,李鹏,富晓鹏等.面向多FPGA实时仿真器的资源优化配置方法[J].电力系统自动化,2023,47(11):88-100.
FU Hao,LI Peng,FU Xiaopeng,et al.Optimal Resource Allocation Method for Real-time Simulator Based on Multiple Field Programmable Gate Arrays[J].Automation of Electric Power Systems,2023,47(11):88-100.
付浩,李鹏,富晓鹏等.面向多FPGA实时仿真器的资源优化配置方法[J].电力系统自动化,2023,47(11):88-100. DOI: 10.7500/AEPS20221031016.
FU Hao,LI Peng,FU Xiaopeng,et al.Optimal Resource Allocation Method for Real-time Simulator Based on Multiple Field Programmable Gate Arrays[J].Automation of Electric Power Systems,2023,47(11):88-100. DOI: 10.7500/AEPS20221031016.
“双碳”背景下高比例新能源和电力电子设备的接入给电力系统实时仿真带来了巨大挑战。针对有限计算资源和实时性约束下新型电力系统的高效精确实时仿真,研究了基于现场可编程门阵列(FPGA)的实时仿真计算任务剖分与资源配置方法。首先,从计算资源需求和实际解算用时两个维度,对实时仿真器不同仿真架构下的解算单元和功能模块进行精细化建模,进而建立实时仿真任务的时-空需求模型。在此基础上,建立多FPGA实时仿真器的仿真优化配置模型,以发挥实时仿真器的最佳解算性能为目标,合理剖分大规模复杂仿真任务,优化配置逻辑资源、存储器等各类计算资源。最后,通过IEEE 123节点以及中国某实际配电系统算例测试,验证了所提时-空需求模型的正确性与优化配置方法的有效性。
The integration of high proportion of renewable energy and power electronics equipment under “carbon emission peak and carbon neutrality” goals brings challenges to the real-time (RT) simulation of power systems. To realize an efficient and accurate RT simulation of the new power system with limited computing resources and RT constraints
the partitioning and resource allocation method for RT simulation computing tasks based on field programmable gate array (FPGA) is studied. First
from the two dimensions of computing resource demand and actual computing time
the solution units and functional modules under different simulation architectures of the RT simulator are finely modeled
and then the spatial-temporal demand model of the simulation task is established. On this basis
the optimal allocation model of the multiple FPGA RT simulator is established. With the goal of giving full play to the optimal solution performance of the RT simulator
the large-scale complex simulation tasks are reasonably divided
and the logical resources
memory and other computing resources are optimally configured. Finally
the correctness of the proposed spatial-temporal demand model and effectiveness of the optimal resource allocation method are verified using the IEEE 123-node test feeder and a practical distribution system in China.
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