Fast Screening of Vulnerable Transmission Lines in Power Grids: A PageRank-Based Approach

Ma, Zhiyuan; Shen, Chen; Liu, Feng; Mei, Shengwei

doi:10.1109/tsg.2017.2785267

Cited by 63 publications

(53 citation statements)

References 28 publications

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“…In Merrill [16], the edge weight is obtained from the line outage distribution factors. In Zhang [15] and Ma [17], the directed edge weights are obtained from both the line flow changes and the remaining margin in the line the power is transferred to. Then Zhang [15] combines the directed edges to give undirected edges.…”

Section: A Literature Reviewmentioning

confidence: 99%

“…Then Zhang [15] combines the directed edges to give undirected edges. On the other hand, Ma [17] retains the directed edges and also represents hidden failures by additional nodes. Third, in Yang [18], the edge corresponds to the correlation between any two lines.…”

Section: A Literature Reviewmentioning

confidence: 99%

“…Various measures are proposed for the identification of critical components based on the influence graph. [11], [12], [17], [23] form their specific measures based on their own influence/interaction graph. Ma [17] uses a modified page-rank algorithm to find critical lines.…”

Section: A Literature Reviewmentioning

confidence: 99%

“…[11], [12], [17], [23] form their specific measures based on their own influence/interaction graph. Ma [17] uses a modified page-rank algorithm to find critical lines. Nakarmi [20] forms the influence graph using methods of both [12] and [18], and proposes a community-based measure to identify critical components.…”

Section: A Literature Reviewmentioning

confidence: 99%

See 3 more Smart Citations

A Markovian Influence Graph Formed From Utility Line Outage Data to Mitigate Large Cascades

Zhou

Dobson

Wang

et al. 2020

IEEE Trans. Power Syst.

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We use observed transmission line outage data to make a Markovian influence graph that describes the probabilities of transitions between generations of cascading line outages. Each generation of a cascade consists of a single line outage or multiple line outages. The new influence graph defines a Markov chain and generalizes previous influence graphs by including multiple line outages as Markov chain states. The generalized influence graph can reproduce the distribution of cascade size in the utility data. In particular, it can estimate the probabilities of small, medium and large cascades. The influence graph has the key advantage of allowing the effect of mitigations to be analyzed and readily tested, which is not available from the observed data. We exploit the asymptotic properties of the Markov chain to find the lines most involved in large cascades and show how upgrades to these critical lines can reduce the probability of large cascades.

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Section: A Literature Reviewmentioning

confidence: 99%

Section: A Literature Reviewmentioning

confidence: 99%

Section: A Literature Reviewmentioning

confidence: 99%

Section: A Literature Reviewmentioning

confidence: 99%

See 2 more Smart Citations

A Markovian Influence Graph Formed From Utility Line Outage Data to Mitigate Large Cascades

Zhou

Dobson

Wang

et al. 2020

IEEE Trans. Power Syst.

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show abstract

“…Combining the pure topological indices and electric system properties, the cascading failures can be mimicked more accurately, which provides a new way to analyze the propagation features of cascading failures. Based on a simplified interaction graph representation of cascading outages, a PageRank-based algorithm is proposed to identify the vulnerable lines in power grids [16]. By calculating the probability that one component failure causes another, an interaction model considering power flow and re-dispatching is proposed to mitigate the cascading failure risk [17].…”

Section: Introductionmentioning

confidence: 99%

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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Exploring The Relationship Between Ordinary PageRank, Lazy PageRank and Random Walk with Backstep PageRank for Different Graph Structures

Biganda¹,

Abola²,

Engström³

et al. 2020

Data Analysis and Applications 3

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Fast Screening of Vulnerable Transmission Lines in Power Grids: A PageRank-Based Approach

Cited by 63 publications

References 28 publications

A Markovian Influence Graph Formed From Utility Line Outage Data to Mitigate Large Cascades

A Markovian Influence Graph Formed From Utility Line Outage Data to Mitigate Large Cascades

Modeling and Fault Propagation Analysis of Cyber–Physical Power System

Exploring The Relationship Between Ordinary PageRank, Lazy PageRank and Random Walk with Backstep PageRank for Different Graph Structures

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