TY - GEN
T1 - Uncovering Power Transmission Dynamic Model from Incomplete PMU Observations
AU - Lokhov, Andrey Y.
AU - Deka, Deepjyoti
AU - Vuffray, Marc
AU - Chertkov, Michael
N1 - Funding Information:
The work is supported by funding from the U.S. DOE/OE as part of the DOE Grid Modernization Initiative and the Center for Nonlinear Studies (CNLS) at Los Alamos National Laboratory.
Publisher Copyright:
© 2018 IEEE.
PY - 2018/7/2
Y1 - 2018/7/2
N2 - Proliferation of Phasor Measurement Units (PMUs) that allow for a synchronous and distributed collection of data can be leveraged to obtain reliable information about the power system model. In practice, one has to account for the system being partially observed as not every bus hosts a PMU. We consider the problem of partial recovery of the underlying dynamic state matrix of transmission power grids from time-stamped PMU measurements on a subset of nodes in a network. We propose a data-driven method which does not assume any knowledge of system parameters and only relies on basic assumptions about the system dynamics. The method is based on a least-squares regression with a nuclear norm regularization that accounts for the effect of hidden observations, supplemented with structural physics-informed constraints that enforce the identifiability. Performance of the method is demonstrated on an IEEE test case example.
AB - Proliferation of Phasor Measurement Units (PMUs) that allow for a synchronous and distributed collection of data can be leveraged to obtain reliable information about the power system model. In practice, one has to account for the system being partially observed as not every bus hosts a PMU. We consider the problem of partial recovery of the underlying dynamic state matrix of transmission power grids from time-stamped PMU measurements on a subset of nodes in a network. We propose a data-driven method which does not assume any knowledge of system parameters and only relies on basic assumptions about the system dynamics. The method is based on a least-squares regression with a nuclear norm regularization that accounts for the effect of hidden observations, supplemented with structural physics-informed constraints that enforce the identifiability. Performance of the method is demonstrated on an IEEE test case example.
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U2 - 10.1109/CDC.2018.8619606
DO - 10.1109/CDC.2018.8619606
M3 - Conference contribution
AN - SCOPUS:85062170191
T3 - Proceedings of the IEEE Conference on Decision and Control
SP - 4008
EP - 4013
BT - 2018 IEEE Conference on Decision and Control, CDC 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 57th IEEE Conference on Decision and Control, CDC 2018
Y2 - 17 December 2018 through 19 December 2018
ER -