Stochastic Analysis of Cascading-Failure Dynamics in Power Grids

Rahnamay-Naeini, Mahshid; Wang, Zhuoyao; Ghani, Nasir; Mammoli, Andrea; Hayat, Majeed M.

doi:10.1109/tpwrs.2013.2297276

Cited by 136 publications

(54 citation statements)

References 20 publications

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“…Here we assume that cascading failures are Markovian [15,16,21], so the failures in SF-network only depend on the current system states. The value calculation and more characteristics based on this assumption will be discussed in Section 2.2.…”

Section: States and Failures Of The Sf-networkmentioning

confidence: 99%

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

Song

et al. 2020

Electric Power Systems Research

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In order to mitigate cascading failure blackout risks in power systems, the critical components whose failures lead to high blackout risks should be identified. In this paper, such critical components are identified by the statefailure network (SF-network) formed by cascading failure chain and loss data, which can be gathered from either utilities or simulations. The failures along the chains are recombined in the SF-network, where each failure is allocated a value that can reveal the blackout risks after their occurrences. Thus, critical failures can be identified in the SF-network where the failures raise up blackout risks, and thus the critical components can be found based on their critical failure risks. The simulation results validate the effectiveness of the proposed method.

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Section: States and Failures Of The Sf-networkmentioning

confidence: 99%

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

Song

et al. 2020

Electric Power Systems Research

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“…It is given by (11) 3) Generator: The salient-pole model is adapted in COSMIC. The active and reactive power outputs are given by the nonlinear equations [41] (12) (13) where , and are the direct axis generator synchronous and transient reactances, respectively. The transient open circuit voltage magnitude [41], , is determined by the differential equation (14) where is the direct axis transient time constant, and is the machine exciter output.…”

Section: ) Equation Formentioning

confidence: 99%

Dynamic Modeling of Cascading Failure in Power Systems

Song¹,

Cotilla‐Sanchez²,

Ghanavati³

et al. 2016

IEEE Trans. Power Syst.

210

131

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The modeling of cascading failure in power systems is difficult because of the many different mechanisms involved; no single model captures all of these mechanisms. Understanding the relative importance of these different mechanisms is an important step in choosing which mechanisms need to be modeled for particular types of cascading failure analysis. This work presents a dynamic simulation model of both power networks and protection systems, which can simulate a wider variety of cascading outage mechanisms, relative to existing quasi-steady state (QSS) models. The model allows one to test the impact of different load models and protections on cascading outage sizes. This paper describes each module of the developed dynamic model and demonstrates how different mechanisms interact. In order to test the model we simulated a batch of randomly selected $N-2$ contingencies for several different static load configurations, and found that the distribution of blackout sizes and event lengths from the proposed dynamic simulator correlates well with historical trends. The results also show that load models have significant impacts on the cascading risks. This dynamic model was also compared against a QSS model based on the dc power flow approximations; we find that the two models largely agree, but produce substantially different results for later stages of cascading.Comment: 8 pages, 9 figures, 5 tables, submitted to IEEE transaction

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“…3. Assume cascading outages are Markovian [11,25], the tree is then a Markovian tree. Each node on Markovian tree represents a state, and each branch on Markovian tree represents a mid-timescale random outage.…”

Section: B Markovian-tree Simulation Of Cascading Outages 1) Markovimentioning

confidence: 99%

“…Different from the quasi-dynamic model which is able to sample more than one outage in each interval, here similar to [11,25,26], each interval D  allows at most one element outage. To ensure the equivalency between the quasi-dynamic model and the Markovian tree model, the mid-timescale interval D  in the Markovian tree is set as 1/ N  of that in the quasi-dynamic model, thus the Markovian tree model is equivalent to the case of sampling up to N  outages during the same period.…”

Section: B Markovian-tree Simulation Of Cascading Outages 1) Markovimentioning

confidence: 99%