Joint Estimation of Topology and Injection Statistics in Distribution Grids With Missing Nodes
Joint Estimation of Topology and Injection Statistics in Distribution Grids With Missing Nodes
复制标题
缺失节点配电网拓扑和注入统计的联合估计
DOI：
10.1109/tcns.2020.2977365
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发表时间：
2018
影响因子：
4.2
通讯作者：
S. Backhaus
中科院分区：
文献类型：
--
作者：
Deepjyoti Deka;M. Chertkov;S. Backhaus
配电网资源的优化运行依赖于对其状态和拓扑结构的准确估计。由于实时仪表的存在有限，对这些量的实际估计是复杂的。本文讨论了一个理论框架，共同估计的操作拓扑结构和统计数据的注入在径向配电网节点电压测量的有限可用性。特别是，我们表明，我们提出的算法是能够证明学习的确切的网格拓扑结构和注入统计在所有未观察到的节点，只要他们不相邻。该算法的设计是基于新的有序的趋势，在节点组的电压幅度波动，是独立的兴趣径向物理流网络。理论上分析了所设计算法的复杂性，并使用测试配电网中的线性和非线性交流潮流样本验证了其性能。
Optimal operation of distribution grid resources relies on accurate estimation of its state and topology. Practical estimation of such quantities is complicated by the limited presence of real-time meters. This article discusses a theoretical framework to jointly estimate the operational topology and statistics of injections in radial distribution grids under limited availability of nodal voltage measurements. In particular, we show that our proposed algorithms are able to provably learn the exact grid topology and injection statistics at all unobserved nodes as long as they are not adjacent. The algorithm design is based on novel ordered trends in voltage magnitude fluctuations at node groups, that are independently of interest for radial physical flow networks. The complexity of the designed algorithms is theoretically analyzed and their performance is validated using both linearized and nonlinear ac power flow samples in test distribution grids.
影响因子：
3
作者：
Cavraro, Guido;Kekatos, Vassilis
通讯作者：
Kekatos, Vassilis