TY - GEN
T1 - Learning in Power Distribution Grids under Correlated Injections
AU - Park, Sejun
AU - Deka, Deepjyoti
AU - Chertkov, Michael
N1 - Funding Information:
S. Park is a Ph.D student at Electrical Engineering Department of KAIST, Daejeon, 34141, Korea. Email: [email protected] D. Deka is with the Theory Division and the Center for Nonlinear Studies of LANL, Los Alamos, NM 87544. Email: [email protected] M. Chertkov is with the Theory Division and the Center for Nonlinear Studies of LANL, Los Alamos, NM 87544 and with Center for Energy Systems of Skoltech, Moskow, 143026, Russia. Email: [email protected] The authors acknowledge the support from the Department of Energy through the Grid Modernization Lab Consortium, and the Center for Non Linear Studies (CNLS) for this work.
Publisher Copyright:
© 2018 IEEE.
PY - 2018/7/2
Y1 - 2018/7/2
N2 - Identifying the operational lines and estimating their impedances are critical problems in distribution grids with applications in fault localization, power flow optimization and others. This paper proposes an exact topology and impedance learning algorithm with low complexity that is able to solve problems using only voltage and injection measurements from the terminal nodes in the grid. The crucial benefit of this approach compared to existing works is that it does not require independence of nodal injections. That is, the proposed algorithm is able to recover the topology and impedances even when injections at the terminal nodes are correlated. In addition, its sample complexity for the accurate recovery is described under the multivariate Gaussian assumption of terminal nodes injections. The performance of our learning algorithm is demonstrated through numerical simulations on both synthetic grids and MATPOWER test grid with linearized and non-linear power flow samples.
AB - Identifying the operational lines and estimating their impedances are critical problems in distribution grids with applications in fault localization, power flow optimization and others. This paper proposes an exact topology and impedance learning algorithm with low complexity that is able to solve problems using only voltage and injection measurements from the terminal nodes in the grid. The crucial benefit of this approach compared to existing works is that it does not require independence of nodal injections. That is, the proposed algorithm is able to recover the topology and impedances even when injections at the terminal nodes are correlated. In addition, its sample complexity for the accurate recovery is described under the multivariate Gaussian assumption of terminal nodes injections. The performance of our learning algorithm is demonstrated through numerical simulations on both synthetic grids and MATPOWER test grid with linearized and non-linear power flow samples.
KW - Distribution networks
KW - Partial observation
KW - Sample complexity
KW - Topology and impedance estimation
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U2 - 10.1109/ACSSC.2018.8645102
DO - 10.1109/ACSSC.2018.8645102
M3 - Conference contribution
AN - SCOPUS:85062984065
T3 - Conference Record - Asilomar Conference on Signals, Systems and Computers
SP - 1863
EP - 1868
BT - Conference Record of the 52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018
A2 - Matthews, Michael B.
PB - IEEE Computer Society
T2 - 52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018
Y2 - 28 October 2018 through 31 October 2018
ER -