标题： 导纳矩阵浓度不等式用于理解不确定电力网络 显示英文标题

标题： Admittance Matrix Concentration Inequalities for Understanding Uncertain Power Networks

摘要： 本文提出了在不确定网络参数下的导纳矩阵和经典线性潮流模型的谱的概率界；例如，概率线路故障。 我们提出的方法引入了概率论中的工具，如具有独立元素的随机矩阵的集中不等式。 它在参数不确定性下为交流潮流方程的常见近似提供了误差界，包括直流和LinDistFlow近似。 显示英文摘要

摘要： This paper presents probabilistic bounds for the spectrum of the admittance matrix and classical linear power flow models under uncertain network parameters; for example, probabilistic line contingencies. Our proposed approach imports tools from probability theory, such as concentration inequalities for random matrices with independent entries. It yields error bounds for common approximations of the AC power flow equations under parameter uncertainty, including the DC and LinDistFlow approximations.