Power System Observability: A Practical Algorithm Using Network Topology

Krumpholz, G. R.; Clements, K.A.; Davis, P.W.

doi:10.1109/tpas.1980.319578

Cited by 377 publications

(217 citation statements)

References 3 publications

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“…III, we present an efficient algorithm to find small sets of meters that, if controlled by the adversary, could cause an unobservable attack. The algorithm is based on the purely topological conditions for observability developed in [4]. As such, it is graph-theoretic in nature and uses techniques of submodular function minimization [5], [6], [7].…”

Section: A Summary Of Results and Contributionsmentioning

confidence: 99%

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Malicious Data Attacks on Smart Grid State Estimation: Attack Strategies and Countermeasures

Kosut

Jia

Thomas

et al. 2010

2010 First IEEE International Conference on Smart Grid Communications

288

196

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Abstract-The problem of constructing malicious data attack of smart grid state estimation is considered together with countermeasures that detect the presence of such attacks. For the adversary, using a graph theoretic approach, an efficient algorithm with polynomial-time complexity is obtained for the design of unobservable malicious data attacks. When the unobservable attack does not exist due to restrictions of meter access, attacks are constructed to minimize the residue energy of attack while guaranteeing a certain level of increase of mean square error. For the control center, a computationally efficient algorithm is derived to detect and localize attacks using the generalized likelihood ratio test regularized by an L1 norm penalty on the strength of attack.

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Section: A Summary Of Results and Contributionsmentioning

confidence: 99%

“…The following theorem gives a simple method for determining a number of meters in g(A) to remove from the network to make it unobservable. The proof relies on [4], which gave an efficient method to determine the observability of a network based only on its topology.…”

Section: B Graph-theoretic Approach To Minimum Size Unobservable Attmentioning

confidence: 99%

Malicious Data Attacks on Smart Grid State Estimation: Attack Strategies and Countermeasures

Kosut

Jia

Thomas

et al. 2010

2010 First IEEE International Conference on Smart Grid Communications

288

196

View full text Add to dashboard Cite

show abstract

“…Under the DC model, forms a GMRF with mean and covariance matrix . Proof: Since the power system is fully connected, the matrix is invertible [27]. Thus, the states can be calculated as , from which the theorem follows.…”

Section: A Gmrf Model For Phasor Anglesmentioning

confidence: 99%

“…Finally, as conditional MI is always nonnegative, one can deduce from (31) that the MI function is also nondecreasing. Now, the above analysis can be immediately extended to a time period of length , where (27) 30 years of experience in teaching and research in the area of electrical power system modeling and control. Her main interest is in the systems aspects of operations, planning, and economics of the electric power industry.…”

Section: B Proof Of Theoremmentioning

confidence: 99%

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An Information-Theoretic Approach to PMU Placement in Electric Power Systems

Cui

Weng

et al. 2013

IEEE Trans. Smart Grid

128

102

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Abstract-This paper presents an information-theoretic approach to address the phasor measurement unit (PMU) placement problem in electric power systems. Different from the conventional 'topological observability' based approaches, this paper advocates a much more refined, information-theoretic criterion, namely the mutual information (MI) between PMU measurements and power system states. The proposed MI criterion not only includes observability as a special case, but also rigorously models the uncertainty reduction on power system states from PMU measurements. Thus, it can generate highly informative PMU configurations. The MI criterion can also facilitate robust PMU placement by explicitly modeling probabilistic PMU outages. We propose a greedy PMU placement algorithm, and show that it achieves an approximation ratio of for any PMU placement budget. We further show that the performance is the best that one can achieve, in the sense that it is NP-hard to achieve any approximation ratio beyond . Such performance guarantee makes the greedy algorithm very attractive in the practical scenario of multi-stage installations for utilities with limited budgets. Finally, simulation results demonstrate near-optimal performance of the proposed PMU placement algorithm.Index Terms-Electric power systems, greedy algorithm, mutual information, phasor measurement unit, submodular functions.

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Dynamic‐State Estimation

Abur

2016

Smart Grid Handbook

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Static state estimators have been in use in control centres for over four decades now. Recent availability of synchronized phasor measurements for bus voltages and line currents allows development of dynamic state estimators which can track the frequency and synchronous machine state variables and complement the information provided by static state estimators on the network states. This chapter provides a brief overview of the static state estimation problem followed by an introduction to dynamic state estimation. The problem of dynamic state estimation is formulated and a dynamic state estimator whose implementation lends itself to large size power grids is described. The chapter also contains a discussion of observability analysis for dynamic state estimators which presents additional challenges due to the time varying and nonlinear nature of the system and measurement equations.

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Power System Observability: A Practical Algorithm Using Network Topology

Cited by 377 publications

References 3 publications

Malicious Data Attacks on Smart Grid State Estimation: Attack Strategies and Countermeasures

Malicious Data Attacks on Smart Grid State Estimation: Attack Strategies and Countermeasures

An Information-Theoretic Approach to PMU Placement in Electric Power Systems

Dynamic‐State Estimation

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