Impacts of Operators’ Behavior on Reliability of Power Grids During Cascading Failures

Wang, Zhuoyao; Rahnamay-Naeini, Mahshid; Abreu, Joana; Shuvro, Rezoan A.; Das, Pankaz; Mammoli, Andrea; Ghani, Nasir; Hayat, Majeed M.

doi:10.1109/tpwrs.2018.2825348

Cited by 35 publications

(22 citation statements)

References 27 publications

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“…However, in addition to the reliability analysis of the physical components of the system, it is also important to consider soft factors, such as human factors and their influences on the system as well as operating policies in reliability analysis and planning. Such interaction analyses will be an essential research direction for future studies of cascading failures and one example of such study is presented in the work in [103].…”

Section: Discussionmentioning

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: Discussionmentioning

confidence: 99%

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

et al. 2020

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“…Human reliability is also important in a highquality maintenance [139]. To embed the operators' behaviours into models of cascaded failures, a probabilistic approach based on Markov chains has been proposed in [140]. [141] studied the impact of frequency of inspections of the system components on the reliability of power grid, and suggested one inspection per year as optimal using a mathematical modelling.…”

Section: A Reliability Of Power Gridsmentioning

confidence: 99%

Power Grids as Complex Networks: Resilience and Reliability Analysis

Amani

Jalili

2021

IEEE Access

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Power grids are cyber-physical systems and can be modelled as network systems where individual units (generators, busbars and loads) are interconnected through physical and cyber links. Network components (nodes/edges) may undergo intentional and/or random failures. In catastrophic cases, a failure initiating from a small set of these components can quickly propagate through the whole network, leading to a cascade of failures that might force a deep whole-grid blackout. Often network components have different vitality and protecting some is more critical than others. This manuscript aims to provide a focused overview of modelling power grids as complex networks and their resilience and reliability analysis. We also perform a critical review of vitality metrics and their precision in power grid resilience analysis. The review is accompanied by some simulations on benchmark and real power grids to show the applicability of these concepts in studying resilience.

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“…The aggregated Markov process can model the time evolution of the cascade and the distribution of cascade size. In further work, Rahnamay-Naeini reduces the aggregated model to a discrete time Markov chain and generalizes it to model cyber and power interdependent network cascading interactions in [31] and to model operator actions interacting with cascading in [32].…”

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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Impacts of Operators’ Behavior on Reliability of Power Grids During Cascading Failures

Cited by 35 publications

References 27 publications

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

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

Power Grids as Complex Networks: Resilience and Reliability Analysis

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

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