EXPOSE the Line Failures Following a Cyber-Physical Attack on the Power Grid

Soltan, Saleh; Zussman, Gil

doi:10.1109/tcns.2018.2844244

Cited by 22 publications

(12 citation statements)

References 31 publications

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“…LR attacks are launched by false load data to affect system operation schedules. The tempered load meter reading deviates from the real reading and thus system operators make decisions based on the falsified load demand [18][19][20][21]. Consequently, this can cause economic loss and physical damages to the equipment.…”

Section: Modelling Of Load Redistribution Attacksmentioning

confidence: 99%

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Cyber-Resilience Enhancement and Protection for Uneconomic Power Dispatch Under Cyber-Attacks

Zhao

Ding

et al. 2021

IEEE Trans. Power Delivery

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False data injection (FDI), could cause severe uneconomic system operation and even large blackout, which is further compounded by the increasingly integrated fluctuating renewable generation. As a commonly conducted type of FDI, load redistribution (LR) attack is judiciously manipulated by attackers to alter the load measurement on power buses and affect the normal operation of power systems. In particular, LR attacks have been proved to easily bypass the detection of state estimation. This paper presents a novel distributionally robust optimization (DRO) for operating transmission systems against cyber-attacks while considering the uncertainty of renewable generation. The FDI imposed by an adversary aims to maximally alter system parameters and mislead system operations while the proposed optimization method is used to reduce the risks caused by FDI. Unlike the worst-case-oriented robust optimization, DRO neglects the extremely low-probability case and thus weakens the conservatism, resulting in more economical operation schemes. To obtain computational tractability, a semidefinite programming problem is reformulated and a constraint generation algorithm is utilized to efficiently solve the original problem in a hierarchical master and sub-problem framework. The proposed method can produce more secure and economic operation for the system of rich renewable under LR attacks, reducing load shedding and operation cost to benefit end customers, network operators, and renewable generation.

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Section: Modelling Of Load Redistribution Attacksmentioning

confidence: 99%

“…State estimation is a significant approach to filter and detect FDI in case that it misleads the operation and control of power systems by inferring with metered operational parameters [16][17][18]. However, it is vulnerable to malicious attacks [9,19], such as load redistribution (LR).…”

Section: Introductionmentioning

confidence: 99%

Cyber-Resilience Enhancement and Protection for Uneconomic Power Dispatch Under Cyber-Attacks

Zhao

Ding

et al. 2021

IEEE Trans. Power Delivery

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“…In [12], a heuristic algorithm was proposed to handle cyber attacks that distort sensor data or inject stealthy data. Moreover, [13] modified the algorithm and the theoretical guarantees in [2] according to the AC power flow model. However, the above works were all based on the assumption that the power grid remained connected after the failures, which may not be true under multi-link failures [4].…”

Section: A Related Workmentioning

confidence: 99%

Verifiable Failure Localization in Smart Grid under Cyber-Physical Attacks

Huang,

He,

Chaudhuri

et al. 2021

Preprint

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Cyber-physical attacks impose a significant threat to the smart grid, as the cyber attack makes it difficult to identify the actual damage caused by the physical attack. To defend against such attacks, various inference-based solutions have been proposed to estimate the states of grid elements (e.g., transmission lines) from measurements outside the attacked area, out of which a few have provided theoretical conditions for guaranteed accuracy. However, these conditions are usually based on the ground truth states and thus not verifiable in practice. To solve this problem, we develop (i) verifiable conditions that can be tested based on only observable information, and (ii) efficient algorithms for verifying the states of links (i.e., transmission lines) within the attacked area based on these conditions. Our numerical evaluations based on the Polish power grid and IEEE 300-bus system demonstrate that the proposed algorithms are highly successful in verifying the states of truly failed links, and can thus greatly help in prioritizing repairs during the recovery process.

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“…Each sequence consists of (i) a fully observable [T − 1]-long time series containing the power and voltage phasors for all nodes (i.e., s(τ ) and v(τ ) for τ ∈ {t − 1 − T, • • • , t − 1}), (ii) a partially observable time step containing N s (t) power phasors, s(t), and (iii) a corresponding target voltage vector, v(t). Each power and voltage component (real and imaginary) in the data set was standardized using the mean and the standard deviation obtained from the training set 4 . An illustration of the full datapreparation scheme is depicted in Fig.…”

Section: A Experimental Design and Data Preparationmentioning

confidence: 99%

Physics-Informed Deep Neural Network Method for Limited Observability State Estimation

Ostrometzky¹,

Berestizshevsky²,

Bernstein³

et al. 2019

Preprint

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The precise knowledge regarding the state of the power grid is important in order to ensure optimal and reliable grid operation. Specifically, knowing the state of the distribution grid becomes increasingly important as more renewable energy sources are connected directly into the distribution network, increasing the fluctuations of the injected power.In this paper, we consider the case when the distribution grid becomes partially observable, and the state estimation problem is under-determined. We present a new methodology that leverages a deep neural network (DNN) to estimate the grid state. The standard DNN training method is modified to explicitly incorporate the physical information of the grid topology and line/shunt admittance. We show that our method leads to a superior accuracy of the estimation when compared to the case when no physical information is provided. Finally, we compare the performance of our method to the standard state estimation approach, which is based on the weighted least squares with pseudo-measurements, and show that our method performs significantly better with respect to the estimation accuracy.

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EXPOSE the Line Failures Following a Cyber-Physical Attack on the Power Grid

Cited by 22 publications

References 31 publications

Cyber-Resilience Enhancement and Protection for Uneconomic Power Dispatch Under Cyber-Attacks

Cyber-Resilience Enhancement and Protection for Uneconomic Power Dispatch Under Cyber-Attacks

Verifiable Failure Localization in Smart Grid under Cyber-Physical Attacks

Physics-Informed Deep Neural Network Method for Limited Observability State Estimation

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