Estimation of faults in DC electrical power system

Gorinevsky, D.; Boyd, Stephen; Poll, Scott

doi:10.1109/acc.2009.5160301

Cited by 44 publications

(26 citation statements)

References 28 publications

(44 reference statements)

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“…The constant ν needs to be adjusted until the solution involves an a with sparsity k. This requires solving (22) several times. A similar approach was taken in [30].…”

Section: Generalized Likelihood Ratio Detector With L 1 Norm Regulmentioning

confidence: 99%

Malicious Data Attacks on the Smart Grid

Kosut

Jia

Thomas

et al. 2011

IEEE Trans. Smart Grid

713

430

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Abstract-Malicious attacks against power systems are investigated, in which an adversary controls a set of meters and is able to alter the measurements from those meters. Two regimes of attacks are considered. The strong attack regime is where the adversary attacks a sufficient number of meters so that the network state becomes unobservable by the control center. For attacks in this regime, the smallest set of attacked meters capable of causing network unobservability is characterized using a graph theoretic approach. By casting the problem as one of minimizing a supermodular graph functional, the problem of identifying the smallest set of vulnerable meters is shown to have polynomial complexity. For the weak attack regime where the adversary controls only a small number of meters, the problem is examined from a decision theoretic perspective for both the control center and the adversary. For the control center, a generalized likelihood ratio detector is proposed that incorporates historical data. For the adversary, the tradeoff between maximizing estimation error at the control center and minimizing detection probability of the launched attack is examined. An optimal attack based on minimum energy leakage is proposed.Index Terms-Power system state estimation, false data attack, bad data detection, power network observability, smart grid security.

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“…The constant ν needs to be adjusted until the solution involves an a with sparsity k. This requires solving (22) several times. A similar approach was taken in [30].…”

Section: Generalized Likelihood Ratio Detector With L 1 Norm Regulmentioning

confidence: 99%

Malicious Data Attacks on the Smart Grid

Kosut

Jia

Thomas

et al. 2011

IEEE Trans. Smart Grid

713

430

View full text Add to dashboard Cite

show abstract

“…The main difference between the approach in [15] and ours is that the formulation in [15] has the interpretation that the attack vector has the Laplacian prior and the state variables deterministic whereas, in this paper, the state vector is Gaussian and attack vector deterministic.…”

Section: B Related Workmentioning

confidence: 99%

“…Another relevant recent work is by Gorinevsky, Boyd, and Poll [15] where a quadratic programming formulation for estimating faults is presented. The main difference between the approach in [15] and ours is that the formulation in [15] has the interpretation that the attack vector has the Laplacian prior and the state variables deterministic whereas, in this paper, the state vector is Gaussian and attack vector deterministic.…”

Section: B Related Workmentioning

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

287

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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“…[12] examines the diagnostics for the international space station, [10] for an aircraft electric system, and [7] for a marine vehicle power system. For a DC system, [8] uses an optimization-based approach to estimate fault states.…”

Section: Introductionmentioning

confidence: 99%

Dynamic state estimation in distributed aircraft electric control systems via adaptive submodularity

Maillet

Özay

et al. 2013

52nd IEEE Conference on Decision and Control

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Abstract-We consider the problem of estimating the discrete state of an aircraft electric system under a distributed control architecture through active sensing. The main idea is to use a set of controllable switches to reconfigure the system in order to gather more information about the unknown state. By adaptively making a sequence of reconfiguration decisions with uncertain outcome, then correlating measurements and prior information to make the next decision, we aim to reduce the uncertainty. A greedy strategy is developed that maximizes the one-step expected uncertainty reduction. By exploiting recent results on adaptive submodularity, we give theoretical guarantees on the worst-case performance of the greedy strategy. We apply the proposed method in a fault detection scenario where the discrete state captures possible faults in various circuit components. In addition, simple abstraction rules are proposed to alleviate state space explosion and to scale up the strategy. Finally, the efficiency of the proposed method is demonstrated empirically on different circuits.

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Estimation of faults in DC electrical power system

Cited by 44 publications

References 28 publications

Malicious Data Attacks on the Smart Grid

Malicious Data Attacks on the Smart Grid

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

Dynamic state estimation in distributed aircraft electric control systems via adaptive submodularity

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