A state-failure-network method to identify critical components in power systems

Li, Linzhi; Wu, Hao; Song, Yonghua; Liu, Yi

doi:10.1016/j.epsr.2019.106192

Cited by 20 publications

(24 citation statements)

References 31 publications

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“…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]. This novel taxonomy is used to classify thirty detailed research studies, including conference and journal publications, in the data-driven category into various subcategories.…”

Section: Review Methodologymentioning

confidence: 99%

“…Consecutive Failures [13][14][15][16][17][18][19][20] Generation-based Failures [21][22][23][24][25][26] Influence-based [27][28][29][30] Multiple and Simultaneous Failures [31][32][33][34][35][36][37] Risk-graph [38][39][40][41] Correlation-based [29,30,42]…”

Section: Data-driven Interaction Graphsmentioning

confidence: 99%

“…Other examples of methods that consider interactions of multiple failures at the same time include [34][35][36][37]. In these works, fault chains are used to construct a state failure network.…”

Section: Interaction Graph Of Multiple and Simultaneous Failuresmentioning

confidence: 99%

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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%

Section: Data-driven Interaction Graphsmentioning

confidence: 99%

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

et al. 2020

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“…Li and Wu [27] combine simulated fault chains into a network and use reinforcement learning to explore, evaluate, and find chains most critical to load shed. In further work, Li and Wu [28] combine simulated fault chains into a state-failure network from which expected load shed can be computed for each state and failure by propagating load shed backwards accounting for the transition probabilities of the edges. The transition probabilities are estimated similarly to an influence graph by the relative frequency of that transition at that stage of the data.…”

Section: A Literature Reviewmentioning

confidence: 99%

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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“…To improve the efficiency for obtaining the blackout risks after line capacity temporary expansion is implemented, another way using cascading failures data to form data based models has been proposed in the literature [22]- [24]. In order to quantify the impact of line capacity temporary expansion on blackout risks, the proper data based model should be able to accurately figure out the blackout risks after the line capacities are expanded.…”

Section: Introductionmentioning

confidence: 99%

Quantify the Impact of Line Capacity Temporary Expansion on Blackout Risk by the State-Failure–Network Method

Song

et al. 2019

IEEE Access

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The blackout risks of cascading failures in power systems are notably associated with the failures of transmission lines. Line capacity temporary expansion can reduce blackout risk by decreasing the line failures due to the overloads during the cascading failures. To efficiently quantify the impact of line capacity temporary expansion on blackout risks, we propose a data based state-failure-network method in this paper. The state-failure network, which is formed by the cascading failure data generated by cascading failure simulations, contains the empirical probabilities that correspond to the failure probabilities of lines. Since implementing line capacity temporary expansion to the system can change the failure probabilities of lines and reduce the blackout risk, the empirical probabilities offer the link between line capacity temporary expansion and state-failure network. By updating the values in the state-failure network with changed empirical probabilities, the blackout risk after line capacity temporary expansion is implemented can be efficiently worked out by state-failure network. Thus, the impact of line capacity temporary expansion on blackout risk is quantified by comparing the newly calculated blackout risk with the risk before the line capacity temporary expansion is implemented. The advantage of the proposed method lies in the high accuracy and efficiency of quantifying the impacts of any line capacity temporary expansion schemes once the state-failure network is formed. Case studies verify the accuracy and efficiency of the proposed method.INDEX TERMS Blackout risk, cascading failures, state-failure-network method, line capacity temporary expansion.

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A state-failure-network method to identify critical components in power systems

Cited by 20 publications

References 31 publications

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

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

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

Quantify the Impact of Line Capacity Temporary Expansion on Blackout Risk by the State-Failure–Network Method

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