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Fine-Grained Network Decomposition for Massively Parallel Electromagnetic Transient Simulation of Large Power Systems

机译:大功率系统大规模并行电磁暂态仿真的细粒度网络分解

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摘要

Electromagnetic transient (EMT) simulation is one of the most complex power system studies that requires detailed modeling of the study system including all frequency-dependent and nonlinear effects. Large-scale EMT simulation is becoming commonplace due to the increasing growth and interconnection of power grids, and the need to study the impact of system events of the wide area network. To cope with enormous computational burden, the massively parallel architecture of the graphics processing unit (GPU) is exploited in this paper for large-scale EMT simulation. A fine-grained network decomposition, called shattering network decomposition, is proposed to divide the power system network exploiting its topological and physical characteristics into linear and nonlinear networks, which adapt to the unique features of the GPU-based massive thread computing system. Large-scale systems, up to 240 000 nodes, with typical components, including synchronous machines, transformers, transmission lines, and nonlinear elements, and multiple levels modular multilevel converter with up to 6144 submodules, are tested and compared with mainstream simulation software to verify the accuracy and demonstrate the speed-up improvement with respect to sequential computation.
机译:电磁暂态(EMT)仿真是最复杂的电力系统研究之一,需要对研究系统进行详细建模,包括所有与频率相关的影响和非线性影响。由于电网的增长和互连不断增长,以及需要研究广域网的系统事件的影响,大规模的EMT仿真正变得司空见惯。为了应付巨大的计算负担,本文将图形处理单元(GPU)的大规模并行体系结构用于大规模EMT仿真。提出了一种称为“粉碎网络分解”的细粒度网络分解,将利用其拓扑和物理特性的电力系统网络划分为线性和非线性网络,以适应基于GPU的大规模线程计算系统的独特功能。测试了多达24万个节点的大型系统,其典型组件包括同步电机,变压器,传输线和非线性元件,以及具有多达6144个子模块的多级模块化多级转换器,并与主流仿真软件进行了比较,以验证精度,并证明了在顺序计算方面的提速改进。

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