Critical Component Analysis in Cascading Failures for Power Grids Using Community Structures in Interaction Graphs

Nakarmi, Upama; Rahnamay-Naeini, Mahshid; Khamfroush, Hana

doi:10.1109/tnse.2019.2904008

Cited by 28 publications

(32 citation statements)

References 56 publications

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“…As the name implies, the data-driven approaches for building interaction graphs rely on data collected from the system (historical and real data or simulation data) for inferring and characterizing interactions among the components of the system. Further, three categories are defined for data-driven interaction graphs based on the method used for analyzing the data.These include: (1) methods based on outage sequence analysis , (2) risk-graph methods [38][39][40][41], and (3) correlation-based methods [29,30,42,79]. The category of outage sequence analysis is further divided into four sub-categories including (i) consecutive failure-based methods [13][14][15][16][17][18][19][20], (ii) generation-based methods [21][22][23][24][25][26], (iii) influence-based methods [27][28][29][30], and (iv) multiple and simultaneous failure-based methods [31][32][33][34][35][36][37].…”

Section: Review Methodologymentioning

confidence: 99%

Interaction Graphs for Cascading Failure Analysis in Power Grids: A Survey

et al. 2020

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Understanding and analyzing cascading failures in power grids have been the focus of many researchers for years. However, the complex interactions among the large number of components in these systems and their contributions to cascading failures are not yet completely understood. Therefore, various techniques have been developed and used to model and analyze the underlying interactions among the components of the power grid with respect to cascading failures. Such methods are important to reveal the essential information that may not be readily available from power system physical models and topologies. In general, the influences and interactions among the components of the system may occur both locally and at distance due to the physics of electricity governing the power flow dynamics as well as other functional and cyber dependencies among the components of the system. To infer and capture such interactions, data-driven approaches or techniques based on the physics of electricity have been used to develop graph-based models of interactions among the components of the power grid. In this survey, various methods of developing interaction graphs as well as studies on the reliability and cascading failure analysis of power grids using these graphs have been reviewed.

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Section: Review Methodologymentioning

confidence: 99%

Interaction Graphs for Cascading Failure Analysis in Power Grids: A Survey

et al. 2020

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“…In [19], Carreras constructs a synchronization matrix from simulation data from the OPA model to identify the lines with higher overloading probabilities. Other papers [13], [14], [20]- [22] form their graph of interactions similarly to the above methods. In this paper, we base the influence graph edges on conditional probabilities.…”

Section: A Literature Reviewmentioning

confidence: 99%

“…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. [20] compares its measure with other centrality measures based on network theory, and concludes that its method performs better than other methods in most cases.…”

Section: A Literature Reviewmentioning

confidence: 99%

“…Nakarmi [20] forms the influence graph using methods of both [12] and [18], and proposes a community-based measure to identify critical components. [20] compares its measure with other centrality measures based on network theory, and concludes that its method performs better than other methods in most cases. In this paper, our influence graph is a rigorous Markov chain, and the identification of critical lines is based on the asymptotic quasi-stationary distribution.…”

Section: A Literature Reviewmentioning

confidence: 99%

“…If the outage data is simulated, the selection of initial system states matters. For example, Nakarami [20] shows that different system states lead to different influence graphs. In this paper, we form our influence graph from fourteen years of public outage data of a specific area, so that our influence graph reflects the initial faults and states encountered over that period of time in that power system area.…”

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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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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