TY - GEN
T1 - Blind estimation of states and topology (BEST) in power systems
AU - Gera, Idan
AU - Yakoby, Yair
AU - Routtenberg, Tirza
N1 - Funding Information:
V. CONCLUSION In this paper we introduce two novel methods for blind estimation of states and topology in power systems, given active power measurements. The first method, Cov-BEST, is based on using the states’ SOS and the positive-definiteness of the reduced Laplacian matrix. The second method, GLS-BEST, is a two-stage method that performs a conventional BSS on the power measurements and then, resolves the inherent permutation and scaling ambiguities by using the unique properties of the Laplacian topology matrix and knowledge of the value range of the states. The proposed methods are non-iterative methods and, thus, do not suffer from problems of convergence and initial guess. Simulations show that the proposed methods are feasible and succeed in reconstructing the topology and estimating the states, and that the state estimator by the Cov-BEST converges to the oracle state estimator, which assumes perfect knowledge of the topology. Topics for future research include incorporating sparsity constraints and assuming a noisy measurement model. ACKNOWLEDGMENT This work is partially supported by the ISRAEL SCIENCE FOUNDATION (ISF), grant No. 1173/16.
Funding Information:
This work is partially supported by the Israel SCIENCE FOUNDATION (ISF), grant No. 1173/16.
Publisher Copyright:
© 2017 IEEE.
PY - 2018/3/7
Y1 - 2018/3/7
N2 - In this paper, we consider the problem of state estimation and topology identification in power systems. We assume the DC model of real power measurements with unknown voltage phases and an unknown admittance matrix. We show that this problem is equivalent to the blind source separation (BSS) problem, where the mixing matrix is a weighted Laplacian matrix. We propose two new Blind Estimation of States and Topology (BEST) methods for this problem. The first method, Cov-BEST, is based on utilizing the states' second-order statistics and the positive-definiteness of the reduced Laplacian matrix. The second method, Generalized Laplacian Separation (GLS)-BEST, is obtained by applying any general BSS method, followed by an approach that resolves the inherent BSS ambiguities by utilizing the Laplacian matrix properties. In contrast to existing methods, the proposed methods achieve full recovery of the topology matrix and are not limited to matrix eigenvectors estimation. The performance of the proposed methods is evaluated for a general network with an arbitrary number of buses and for the IEEE-14 bus system, and compared with the oracle state estimator.
AB - In this paper, we consider the problem of state estimation and topology identification in power systems. We assume the DC model of real power measurements with unknown voltage phases and an unknown admittance matrix. We show that this problem is equivalent to the blind source separation (BSS) problem, where the mixing matrix is a weighted Laplacian matrix. We propose two new Blind Estimation of States and Topology (BEST) methods for this problem. The first method, Cov-BEST, is based on utilizing the states' second-order statistics and the positive-definiteness of the reduced Laplacian matrix. The second method, Generalized Laplacian Separation (GLS)-BEST, is obtained by applying any general BSS method, followed by an approach that resolves the inherent BSS ambiguities by utilizing the Laplacian matrix properties. In contrast to existing methods, the proposed methods achieve full recovery of the topology matrix and are not limited to matrix eigenvectors estimation. The performance of the proposed methods is evaluated for a general network with an arbitrary number of buses and for the IEEE-14 bus system, and compared with the oracle state estimator.
KW - Blind source separation (BSS)
KW - Laplacian mixing matrix
KW - Power system monitoring
KW - State estimation
KW - Topology identification
UR - http://www.scopus.com/inward/record.url?scp=85048004172&partnerID=8YFLogxK
U2 - 10.1109/GlobalSIP.2017.8309127
DO - 10.1109/GlobalSIP.2017.8309127
M3 - Conference contribution
AN - SCOPUS:85048004172
T3 - 2017 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017 - Proceedings
SP - 1080
EP - 1084
BT - 2017 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017 - Proceedings
PB - Institute of Electrical and Electronics Engineers
T2 - 5th IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017
Y2 - 14 November 2017 through 16 November 2017
ER -