TY - JOUR
T1 - Topology Estimation Using Graphical Models in Multi-Phase Power Distribution Grids
AU - Deka, Deepjyoti
AU - Chertkov, Michael
AU - Backhaus, Scott
N1 - Funding Information:
Thisworkwas supported by the U.S. Department of Energy Grid Modernization Initiative and the Center for Non Linear Studies at Los Alamos. Paper no. TPWRS-00473-2018
Funding Information:
Manuscript received April 9, 2018; revised October 19, 2018; accepted January 18, 2019. Date of publication February 1, 2019; date of current version April 22, 2020. This work was supported by the U.S. Department of Energy Grid Modernization Initiative and the Center for Non Linear Studies at Los Alamos. Paper no. TPWRS-00473-2018. (Corresponding author: Deepjyoti Deka.) D. Deka and S. Backhaus are with the Los Alamos National Laboratory, Los Alamos, NM 87544 USA (e-mail: [email protected]; [email protected]).
Publisher Copyright:
© 1969-2012 IEEE.
PY - 2020/5
Y1 - 2020/5
N2 - 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.
AB - 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.
KW - Distribution networks
KW - computational complexity
KW - conditional independence
KW - graphical models
KW - unbalanced three-phase
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U2 - 10.1109/TPWRS.2019.2897004
DO - 10.1109/TPWRS.2019.2897004
M3 - Article
AN - SCOPUS:85081108623
SN - 0885-8950
VL - 35
SP - 1663
EP - 1673
JO - IEEE Transactions on Power Systems
JF - IEEE Transactions on Power Systems
IS - 3
M1 - 8632741
ER -