Variational analysis of large power grids by exploring statistical sampling sharing and spatial locality
Variational analysis of large power grids by exploring statistical sampling sharing and spatial locality
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DOI：
10.1109/iccad.2005.1560146
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发表时间：
2005-05
期刊：
影响因子：
--
通讯作者：
Peng Li
中科院分区：
文献类型：
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作者：
Peng Li
提出了一种参数随机游走算法，以便于在电网中存在大量变化源的情况下对少数关键网络节点进行可行性评估。通过将统计抽样共享和随机游走相结合，我们设计了一种高效的大型配电网局部灵敏度分析方法，使得该分析无需求解整个网络即可进行。我们进一步证明了这种基于抽样的参数分析可以从一阶灵敏度分析扩展到更精确的二阶分析。通过利用我们算法公式中固有的自然空间局部性，即使对于大量的全局和局部变化源，二阶参数分析也可以非常有效地进行。通过分析大电网在工艺和电流负载变化的影响下所提出的方法，证明了该方法是完全不可行的。实验结果表明，该算法在准确率和运行时间上都具有较好的性能。
We propose a parametric random walk algorithm to facilitate a feasible evaluation of a few critical network nodes under the influence of a large number of variation sources in a power grid. By combining statistical sampling sharing with random walks, we devise an efficient localized sensitivity analysis for large power distribution networks such that the analysis can be conducted without solving the complete network. We further show that this sampling-based parametric analysis can be extended from the first order sensitivity analysis to a more accurate second order analysis. By exploiting the natural spatial locality inherent in our algorithm formulation, the second order parametric analysis can be conducted very efficiently even for a large number of global and local variation sources. The proposed approach is demonstrated by analyzing large power grids under the influence of process and current loading variations to which the application of the standard brutal-force circuit simulation becomes completely infeasible. Our results have demonstrated the superior performance of the proposed algorithm both in terms of accuracy and runtime.