Recovering missing data via matrix completion in electricity distribution systems

2019 IEEE Data Science Workshop (DSW)

et al. 2019

Self Cite

We study the recovery of missing data from multiple smart grid datasets within a matrix completion framework. The datasets contain the electrical magnitudes required for monitoring and control of the electricity distribution system. Each dataset is described by a low rank matrix. Different datasets are correlated as a result of containing measurements of different physical magnitudes generated by the same distribution system. To assess the validity of matrix completion techniques in the recovery of missing data, we characterize the fundamental limits when two correlated datasets are jointly recovered. We then proceed to evaluate the performance of Singular Value Thresholding (SVT) and Bayesian SVT (BSVT) in this setting. We show that BSVT outperforms SVT by simulating the recovery for different correlated datasets. The performance of BSVT displays the tradeoff behaviour described by the fundamental limit, which suggests that BSVT exploits the correlation between the datasets in an efficient manner.

Section: Real Data Modelsupporting

confidence: 90%

Section: Singular Value Theresholdingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Recovery of Missing Data in Correlated Smart Grid Datasets

Genes

2019 IEEE Data Science Workshop (DSW)

et al. 2019

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“…Bayesian frameworks are considered for transmission grids in [9] and [10] and for distribution grids in [11] by assuming a multivariate Gaussian distribution for the state variables. In this case the operator performs minimum mean square error (MMSE) estimation.…”

Section: Introductionmentioning

confidence: 99%

Information-theoretic attacks in the smart grid

Sun

2017 IEEE International Conference on Smart Grid Communications (SmartGridComm)

et al. 2017

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Gaussian random attacks that jointly minimize the amount of information obtained by the operator from the grid and the probability of attack detection are presented. The construction of the attack is posed as an optimization problem with a utility function that captures two effects: firstly, minimizing the mutual information between the measurements and the state variables; secondly, minimizing the probability of attack detection via the Kullback-Leibler divergence between the distribution of the measurements with an attack and the distribution of the measurements without an attack. Additionally, a lower bound on the utility function achieved by the attacks constructed with imperfect knowledge of the second order statistics of the state variables is obtained. The performance of the attack construction using the sample covariance matrix of the state variables is numerically evaluated. The above results are tested in the IEEE 30-Bus test system.

“…The complex nature of the power system leads naturally to a stochastic modelling of the state variables describing the grid. For instance, the state variables of low voltage distribution systems are well described as following a multivariate Gaussian distribution [11]. DIAs within a Bayesian framework with minimum mean square error (MMSE) estimation are studied in [12] for the centralized case and in [13] for the distributed case.…”

mentioning

confidence: 99%

Stealth Attacks on the Smart Grid

Sun

et al. 2020

IEEE Trans. Smart Grid

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Random attacks that jointly minimize the amount of information acquired by the operator about the state of the grid and the probability of attack detection are presented. The attacks minimize the information acquired by the operator by minimizing the mutual information between the observations and the state variables describing the grid. Simultaneously, the attacker aims to minimize the probability of attack detection by minimizing the Kullback-Leibler (KL) divergence between the distribution when the attack is present and the distribution under normal operation. The resulting cost function is the weighted sum of the mutual information and the KL divergence mentioned above. The trade-off between the probability of attack detection and the reduction of mutual information is governed by the weighting parameter on the KL divergence term in the cost function. The probability of attack detection is evaluated as a function of the weighting parameter. A sufficient condition on the weighting parameter is given for achieving an arbitrarily small probability of attack detection. The attack performance is numerically assessed on the IEEE 30-Bus and 118-Bus test systems.