Efficient large-scale power grid analysis based on preconditioned Krylov-subspace iterative methods

Tsung-Hao Chen, C. C. Chen
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引用次数: 217

Abstract

In this paper, we propose preconditioned Krylov-subspace iterative methods to perform efficient DC and transient simulations for large-scale linear circuits with an emphasis on power delivery circuits. We also prove that a circuit with inductors can be simplified from MNA to NA format, and the matrix becomes an s.p.d. matrix. This property makes it suitable for the conjugate gradient with incomplete Cholesky decomposition as the preconditioner, which is faster than other direct and iterative methods. Extensive experimental results on large-scale industrial power grid circuits show that our method is over 200 times faster for DC analysis and around 10 times faster for transient simulation compared to SPICE3. Furthermore, our algorithm reduces over 75% of memory usage than SPICE3 while the accuracy is not compromised.
基于预条件krylov -子空间迭代法的高效大规模电网分析
在本文中,我们提出了预处理krylov -子空间迭代方法来执行大规模线性电路的有效直流和瞬态仿真,重点是功率传输电路。我们还证明了带电感的电路可以从MNA格式简化为NA格式,并且矩阵变成了sppd矩阵。这一性质使得它适用于以不完全Cholesky分解为预条件的共轭梯度,比其他直接迭代方法更快。大规模工业电网电路的大量实验结果表明,与SPICE3相比,我们的方法在直流分析方面快200倍以上,在瞬态模拟方面快10倍左右。此外,我们的算法比SPICE3减少了75%以上的内存使用,而准确性没有受到影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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