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首页> 外文期刊>Smart Grid, IEEE Transactions on >Full Parallel Power Flow Solution: A GPU-CPU-Based Vectorization Parallelization and Sparse Techniques for Newton–Raphson Implementation
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Full Parallel Power Flow Solution: A GPU-CPU-Based Vectorization Parallelization and Sparse Techniques for Newton–Raphson Implementation

机译：全并行电流解决方案：基于GPU-CPU的矢量化并行化和牛顿Raphson实现的稀疏技术

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摘要

The rapid expansion in scale of power systems and the emergence of new smart grid technologies continuously increase computational complexity of power system simulations. Graphic processing unit (GPU), which features massive concurrent threads and excellent floating-point performance, brings new opportunities into power system simulations. This paper introduces an advanced GPU-CPU based parallel power flow (PF) approach by adopting vectorization parallelization and sparse techniques. Specifically, the root cause behind sparsity property of PF and its impacts on the first two steps of Newton-Raphson (NR) based PF calculation, i.e., forming nodal power mismatch vector and updating Jacobian matrix, are quantitatively analyzed. Moreover, a novel GPU-CPU based parallel PF approach is presented, which effectively integrates advanced GPU-based vectorization parallelization and sparse techniques to accelerate performance of PF calculations. Numerical studies validate the effectiveness of various customized parallel schemes for individual key steps of the proposed NR-based parallel PF approach.
机译：电力系统规模的快速扩张和新的智能电网技术的出现不断提高电力系统模拟的计算复杂性。图形处理单元（GPU），具有大规模的并发线程和出色的浮点性能，带来了新的电力系统模拟机会。本文介绍了一种先进的GPU-CPU基于GPU-CPU的并联电流（PF）方法，采用了矢量化并行化和稀疏技术。具体而言，PF的稀疏性质背后的根本原因及其对基于牛顿-Raphson（NR）的PF计算的前两个步骤的影响，即形成节点功率错配载体和更新雅略族矩阵。此外，介绍了一种新的GPU-CPU的并行PF方法，其有效地集成了基于高级GPU的矢量化并行化和稀疏技术，以加速PF计算的性能。数值研究验证了所提出的基于NR的平行PF方法的各个关键步骤的各种定制并行方案的有效性。

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