Power distribution grids are structurally operated radially, such that energized lines form a collection of trees with a substation at the root of each tree. The operational topology may change from time to time; however, tracking these changes, even though important for the distribution grid operation and control, is hindered by limited real-time monitoring. This paper develops a learning framework to reconstruct the radial operational structure of the distribution grid from synchronized voltage measurements. To detect operational lines, our learning algorithm uses conditional independence tests for continuous random variables that is applicable to a wide class of probability distributions, and in particular Gaussian, for injections. We validate the algorithm through extensive experiments on ac three-phase IEEE distribution grid test cases.

中文翻译：

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在多相配电网中使用图形模型进行拓扑估计

配电网在结构上呈放射状运行，因此通电线路形成一组树木，每棵树的根部都有一个变电站。操作拓扑可能会不时发生变化；然而，跟踪这些变化虽然对配电网的运行和控制很重要，但由于实时监控有限而受到阻碍。本文开发了一个学习框架，用于从同步电压测量中重建配电网的径向运行结构。为了检测操作线，我们的学习算法对连续随机变量使用条件独立性测试，该测试适用于各种概率分布，特别是高斯分布，用于注入。我们通过对交流三相 IEEE 配电网测试用例的大量实验来验证该算法。