智能电网中的线路故障识别：Lasso方法
Lassoing Line Outages in the Smart Power Grid
AI总结：
针对智能电网中多条线路故障的快速识别问题，提出基于Lasso的二次规划算法，通过块坐标下降迭代实现近实时识别。
摘要：
快速准确地揭示电力线路故障对于预防可能导致停电的故障以及智能电网的日常监控和控制任务（包括状态估计和最优潮流）至关重要。现有方法要么受限于所涉及的组合复杂度问题，因此仅限于识别单线和双线故障；要么采用不太实际的假设，例如电网中可用的条件独立相量角度测量。本文仅使用电压相角数据的一个子集，开发了一种近实时算法，用于识别多条线路故障，其复杂度仅为通过块坐标下降迭代求解二次规划。该新方法依赖于将直流线性潮流模型重新表述为稀疏过完备展开，并利用压缩采样和变量选择的最新进展，使用最小绝对收缩和选择算子（Lasso）。对标准IEEE 118总线系统的分析和仿真测试证实了在智能电网中应用Lasso识别线路变化的效果。
英文摘要：
Fast and accurate unveiling of power line outages is of paramount importance not only for preventing faults that may lead to blackouts, but also for routine monitoring and control tasks of the smart grid, including state estimation and optimal power flow. Existing approaches are either challenged by the \emph{combinatorial complexity} issues involved, and are thus limited to identifying single- and double-line outages; or, they invoke less pragmatic assumptions such as \emph{conditionally independent} phasor angle measurements available across the grid. Using only a subset of voltage phasor angle data, the present paper develops a near real-time algorithm for identifying multiple line outages at the affordable complexity of solving a quadratic program via block coordinate descent iterations. The novel approach relies on reformulating the DC linear power flow model as a \emph{sparse} overcomplete expansion, and leveraging contemporary advances in compressive sampling and variable selection using the least-absolute shrinkage and selection operator (Lasso). Analysis and simulated tests on the standard IEEE 118-bus system confirm the effectiveness of lassoing line changes in the smart power grid.
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