@inproceedings{ef5379aa2edc4ce4ba93b7cfd03188d0,
title = "Exact topology and parameter estimation in distribution grids with minimal observability",
abstract = "Limited presence of nodal and line meters in distribution grids hinders their optimal operation and participation in real-time markets. In particular lack of real-time information on the grid topology and infrequently calibrated line parameters (impedances) adversely affect the accuracy of any operational power flow control. This paper suggests a novel algorithm for learning the topology of distribution grid and estimating impedances of the operational lines with minimal observational requirements-it provably reconstructs topology and impedances using voltage and injection measured only at the terminal (end-user) nodes of the distribution grid. All other (intermediate) nodes in the network may be unobserved/hidden. Furthermore no additional input (e.g., number of grid nodes, historical information on injections at hidden nodes) is needed for the learning to succeed. Performance of the algorithm is illustrated in numerical experiments on the IEEE and custom power distribution models.",
keywords = "Distribution networks, Impedance estimation, Missing data, Power flows, Topology learning",
author = "Seiun Park and Deepjyoti Deka and Michael Chcrtkov",
note = "Publisher Copyright: {\textcopyright} 2018 Power Systems Computation Conference.; 20th Power Systems Computation Conference, PSCC 2018 ; Conference date: 11-06-2018 Through 15-06-2018",
year = "2018",
month = aug,
day = "20",
doi = "10.23919/PSCC.2018.8442881",
language = "English (US)",
isbn = "9781910963104",
series = "20th Power Systems Computation Conference, PSCC 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "20th Power Systems Computation Conference, PSCC 2018",
}