Learning topology of the power distribution grid with and without missing data
Learning topology of the power distribution grid with and without missing data
复制标题
学习有和没有丢失数据的配电网拓扑
DOI：
10.1109/ecc.2016.7810304
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发表时间：
2016
期刊：
影响因子：
--
通讯作者：
M. Chertkov
中科院分区：
文献类型：
--
作者：
Deepjyoti Deka;S. Backhaus;M. Chertkov
配电网是指将电力从变电站输送到负荷的电网部分。从结构上讲，配电网是在几个放射状/树状拓扑中的一个中操作的，这些拓扑是通过在一些线路上打开开关而从原始环状网格图派生出来的。由于实时开关监测设备的数量有限，需要对运行结构进行间接估计。本文提出了一种新的学习算法，该算法仅使用节点电压测量来确定运行的径向结构。该算法基于一个关键结果，即正确的操作结构是原始环形图上最小权生成树问题的最优解，其中所有允许的边/线(开放或闭合)上的权重是边端节点电压差的方差。与已有的工作相比，这种基于生成树的方法不需要线路参数的信息，因此具有显著的低复杂度。此外，对于输入电压测量值仅限于总电网节点的子集的情况，提出了一种改进的学习算法。通过在测试用例上的实验，验证了算法的性能(有无缺失数据)。
Distribution grids refer to the part of the power grid that delivers electricity from substations to the loads. Structurally a distribution grid is operated in one of several radial/tree-like topologies that are derived from an original loopy grid graph by opening switches on some lines. Due to limited presence of real-time switch monitoring devices, the operating structure needs to be estimated indirectly. This paper presents a new learning algorithm that uses only nodal voltage measurements to determine the operational radial structure. The algorithm is based on the key result stating that the correct operating structure is the optimal solution of the minimum-weight spanning tree problem over the original loopy graph where weights on all permissible edges/lines (open or closed) is the variance of nodal voltage difference at the edge ends. Compared to existing work, this spanning tree based approach has significantly lower complexity as it does not require information on line parameters. Further, a modified learning algorithm is developed for cases when the input voltage measurements are limited to only a subset of the total grid nodes. Performance of the algorithms (with and without missing data) is demonstrated by experiments on test cases.