Consideration of Cyber-Physical Interdependencies in Reliability Modeling of Smart Grids

Marashi, Koosha; Sarvestani, Sahra Sedigh; Hurson, Ali R.

doi:10.1109/tsusc.2017.2757911

Cited by 58 publications

(32 citation statements)

References 33 publications

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“…In case 3, we analyze the fault First turning into state 1 in Figure 7, the system operates normally, as shown in Figure 2, at time T. Assuming that an FDI attack occurs on the downlink between the control center and the cyber node connected to branch (5,7), which is represented by , state 1 enters into state 2. In state 2, the breaker of the branch (5,7) trips at time T + 1 by reason of the FDI attack, which leads to the power flow on each branch being redistributed.…”

Section: Casementioning

confidence: 99%

“…In state 7, the control center will make decisions and send control commands after receiving physical system information, except for the breaker information of branch (8,9) at time T + 3. Those commands include reclosing the breaks of branch (5,7), branch (4,5), and branch (6,9), which are First turning into state 1 in Figure 7, the system operates normally, as shown in Figure 2, at time T. Assuming that an FDI attack occurs on the downlink between the control center and the cyber node connected to branch (5,7), which is represented by G f t down (7, 5) = G f t down (5, 7) = 0, state 1 enters into state 2. In state 2, the breaker of the branch (5,7) trips at time T + 1 by reason of the FDI attack, which leads to the power flow on each branch being redistributed.…”

Section: Casementioning

confidence: 99%

“…Smart cyber systems provide better monitoring, transferring, and controlling functions for physical systems [6], but there is nonetheless a trade-off, as risks then not only threaten the physical systems themselves but also the cyber systems. The addition of cyber infrastructures may lead to a grid that fails more frequently, with more severe consequences [7]. Furthermore, increased complexity and connectivity are likely to enlarge the scope affected by accidental faults or malicious attacks [7].…”

Section: Introductionmentioning

confidence: 99%

“…The addition of cyber infrastructures may lead to a grid that fails more frequently, with more severe consequences [7]. Furthermore, increased complexity and connectivity are likely to enlarge the scope affected by accidental faults or malicious attacks [7]. As a result, to guarantee the robustness of the whole system, it is of considerable significance to research the interaction mechanisms between the cyber systems and the physical systems.…”

Section: Introductionmentioning

confidence: 99%

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Modeling and Fault Propagation Analysis of Cyber–Physical Power System

2020

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In cyber–physical power systems (CPPSs), the interaction mechanisms between physical systems and cyber systems are becoming more and more complicated. Their deep integration has brought new unstable factors to the system. Faults or attacks may cause a chain reaction, such as control failure, state deterioration, or even outage, which seriously threatens the safe and stable operation of power grids. In this paper, given the interaction mechanisms, we propose an interdependent model of CPPS, based on a characteristic association method. Utilizing this model, we can study the fault propagation mechanisms when faulty or under cyber-attack. Simulation results quantitatively reveal the propagation process of fault risks and the impacts on the CPPS due to the change of state quantity of the system model.

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Section: Casementioning

confidence: 99%

Section: Casementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Modeling and Fault Propagation Analysis of Cyber–Physical Power System

2020

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“…These components are independent, as one failing does not cause the other to fail, but both need to be functional for the system to function: D = { c 1 S , c 2 S }.This system also has two states: the initial state 11 and the failed superstate 01 10 . 4 Once the system has failed, we are no longer interested in its behavior; thus, for this system, we consider 00 unreachable.…”

Section: (Monotonicity)mentioning

confidence: 99%

Towards Refinement and Generalization of Reliability Models Based on Component States

Jarus

Sarvestani

Hurson

2019

2019 Resilience Week (RWS)

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Complex system design often proceeds in an iterative fashion, starting from a high-level model and adding detail as the design matures. This process can be assisted by metamodeling techniques that automate some model manipulations and check for or eliminate modeling mistakes. Our work focuses on metamodeling reliability models: we describe generalization and refinement operations for these models. Generalization relaxes constraints that may be infeasible or costly to evaluate; refinement adds further detail to produce a model that more closely describes the desired system. We define these operations in terms of operations on system constraints. To illustrate the proposed method, we relate these constraints to a common Markov chainbased reliability modeling formalism.1 In short, the transition probability matrices for consecutive-k-of-n systems are not upper triangular; for more detail, see [1, pp. 344-345].

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Reliability evaluation of smart grid using various classic and metaheuristic clustering algorithms considering system uncertainties

Memari

Karimi

Hashemi‐Dezaki

2021

Int Trans Electr Energ Syst

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The reliability of the smart grid is adversely affected due to system uncertainties. Also, the steadily growing deployment of renewable distributed generation (DG) units increases the uncertainties of smart grids. Hence, it is essential to concern the uncertainties in the field of reliability evaluation of smart grids. Although the Monte Carlo simulation (MCS) has received a significant deal of consideration in the literature, there is a research gap in using the clustering algorithms to assess smart grids' reliability. This article aims to fill such a research gap by proposing a new reliability assessment method, using various clustering algorithms. The benefits from the proposed method's accuracy and fast computation are highlighted, while optimal operation, optimal short-term planning, and repetitive problems should be studied. In this paper,

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Consideration of Cyber-Physical Interdependencies in Reliability Modeling of Smart Grids

Cited by 58 publications

References 33 publications

Modeling and Fault Propagation Analysis of Cyber–Physical Power System

Modeling and Fault Propagation Analysis of Cyber–Physical Power System

Towards Refinement and Generalization of Reliability Models Based on Component States

Reliability evaluation of smart grid using various classic and metaheuristic clustering algorithms considering system uncertainties

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