Centralized Identification of Imbalances in Power Networks With Synchrophasor Data

Routtenberg, Tirza; Eldar, Yonina C.

doi:10.1109/tpwrs.2017.2714692

Cited by 15 publications

(17 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Practical methods to approximate the ML estimator ofL,L (ML) , are developed in the next section. Under the model from (8) and the Gaussian-distributed sources assumptions, the normalized log likelihood ofp[n], n = 0, . .…”

Section: System Identification: ML Estimation Of the Mixing Matrixmentioning

confidence: 99%

Power Systems Topology and State Estimation by Graph Blind Source Separation

Grotas

Yakoby

Gera

et al. 2019

IEEE Trans. Signal Process.

Self Cite

View full text Add to dashboard Cite

In this paper, we consider the problem of blind estimation of states and topology (BEST) in power systems. We use the linearized DC model of real power measurements with unknown voltage phases (i.e. states) and an unknown admittance matrix (i.e. topology) and show that the BEST problem can be formulated as a blind source separation (BSS) problem with a weighted Laplacian mixing matrix. We develop the constrained maximum likelihood (ML) estimator of the Laplacian matrix for this graph BSS (GBSS) problem with Gaussian-distributed states. The ML-BEST is shown to be only a function of the states' second-order statistics. Since the topology recovery stage of the ML-BEST approach results in a high-complexity optimization problem, we propose two low-complexity methods to implement it: (1) Two-phase topology recovery, which is based on solving the relaxed convex optimization and then finding the closest Laplacian matrix, and (2) Augmented Lagrangian topology recovery. We derive a closed-form expression for the associated Cramér-Rao bound (CRB) on the topology matrix estimation. The performance of the proposed methods is evaluated for the IEEE-14 bus test-case system and for a random network. It is shown that, asymptotically, the state estimation performance of the proposed ML-BEST methods coincides with the oracles minimum mean-squared-error (MSE) state estimator, and the MSE of the topology estimation achieves the proposed CRB.

show abstract

Section: System Identification: ML Estimation Of the Mixing Matrixmentioning

confidence: 99%

Power Systems Topology and State Estimation by Graph Blind Source Separation

Grotas

Yakoby

Gera

et al. 2019

IEEE Trans. Signal Process.

Self Cite

View full text Add to dashboard Cite

show abstract

“…using the mean value of all total variations derived from the historic grid states according to (10), and where σ R S is the standard deviation between the variations of different historical sates scaled by the parameter α σ,S that defines the confidence interval.…”

Section: Detection Methodsmentioning

confidence: 99%

“…As given in (1), the diagonal elements in the Laplacian matrix, Y k,k , sum up the weights of all edges connected to node k. The smaller this value is, the more "loosely" coupled is the bus. For such buses, even large differences between v k and v l do not contribute significantly to the total variation as it is defined in (10). In summary, for buses with small diagonal values in Y compared to the other buses, attacks are the most difficult to detect.…”

Section: A Detection Characteristicsmentioning

confidence: 99%

“…A sufficient smoothness of the undisturbed signal is a necessary condition for detection after high-pass filtering. The total variation can also be an indicator of the presence of bad data or FDI attacks, as such disturbances tend to increase the total variation defined in (10). As a visualization, in Figs.…”

Section: A Smooth Graphsmentioning

confidence: 99%

“…For detection based on the smoothness, the boundary condition for detectability is given by (10). As long as the attack does not increase S, it cannot be detected.…”

Section: A Detection Characteristicsmentioning

confidence: 99%

See 2 more Smart Citations