Structure Learning in Power Distribution Networks

Deka, Deepjyoti¹  Chertkov, Michael²  Backhaus, Scott N.² 

[1] Univ. of Texas, Austin, TX (United States);Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

关键词: power distribution networks;    power flows;    structure/graph learning;    voltage measurements;    transmission lines;   

DOI  :  10.2172/1167238
RP-ID  :  LA-UR--15-20213
PID  :  OSTI ID: 1167238

学科分类：电力

美国|英语

来源: SciTech Connect

【 摘 要 】

Traditionally power distribution networks are either not observable or only partially observable. This complicates development and implementation of new smart grid technologies, such as these related to demand response, outage detection and management, and improved load-monitoring. Here, inspired by proliferation of the metering technology, we discuss statistical estimation problems in structurally loopy but operationally radial distribution grids consisting in learning operational layout of the network from measurements, e.g. voltage data, which are either already available or can be made available with a relatively minor investment. Our newly suggested algorithms apply to a wide range of realistic scenarios. The algorithms are also computationally efficient ??? polynomial in time ??? which is proven theoretically and illustrated computationally on a number of test cases. The technique developed can be applied to detect line failures in real time as well as to understand the scope of possible adversarial attacks on the grid.