TY - GEN
T1 - ESTIMATION OF THE ADMITTANCE MATRIX IN POWER SYSTEMS UNDER LAPLACIAN AND PHYSICAL CONSTRAINTS
AU - Halihal, Morad
AU - Routtenberg, Tirza
N1 - Funding Information:
This research was supported by the Israel Ministry of Infrastructure, Energy, and Water Resources and by the BGU Cyber Security Research Center.
Publisher Copyright:
© 2022 IEEE
PY - 2022/1/1
Y1 - 2022/1/1
N2 - Admittance matrix estimation in power networks enables faster control actions following emergency scenarios, energy-saving, and other economic and security advantages. In this paper, our goal is to estimate the network admittance matrix, i.e. to learn connectivity and edge weights in the graph representation, under physical and Laplacian constraints. We use the nonlinear AC power flow measurement model, which is based on Kirchhoff's and Ohm's laws, with power and voltage phasor measurements. In order to recover the complex-valued admittance matrix, we formulate the associated constrained maximum likelihood (CML) estimator as the solution of a constrained optimization problem with Laplacian and sparsity constraints. We develop an efficient solution using the associated alternating direction method of multipliers (ADMM) algorithm with an '1 relaxation. The ADMM algorithm is shown to outperform existing methods in the task of recovering the IEEE 14-bus test case.
AB - Admittance matrix estimation in power networks enables faster control actions following emergency scenarios, energy-saving, and other economic and security advantages. In this paper, our goal is to estimate the network admittance matrix, i.e. to learn connectivity and edge weights in the graph representation, under physical and Laplacian constraints. We use the nonlinear AC power flow measurement model, which is based on Kirchhoff's and Ohm's laws, with power and voltage phasor measurements. In order to recover the complex-valued admittance matrix, we formulate the associated constrained maximum likelihood (CML) estimator as the solution of a constrained optimization problem with Laplacian and sparsity constraints. We develop an efficient solution using the associated alternating direction method of multipliers (ADMM) algorithm with an '1 relaxation. The ADMM algorithm is shown to outperform existing methods in the task of recovering the IEEE 14-bus test case.
KW - Admittance matrix estimation
KW - alternating direction method of multipliers (ADMM)
KW - graph learning
KW - power system topology
KW - topology identification
UR - http://www.scopus.com/inward/record.url?scp=85131261727&partnerID=8YFLogxK
U2 - 10.1109/ICASSP43922.2022.9747489
DO - 10.1109/ICASSP43922.2022.9747489
M3 - Conference contribution
AN - SCOPUS:85131261727
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 5972
EP - 5976
BT - 2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Proceedings
PB - Institute of Electrical and Electronics Engineers
T2 - 47th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022
Y2 - 23 May 2022 through 27 May 2022
ER -