Modeling cascading failure of interdependent critical infrastructure systems using HLA-based co-simulation

Wang, Fei; Magoua, Joseph Jonathan; Li, Nan

doi:10.1016/j.autcon.2021.104008

Cited by 20 publications

(7 citation statements)

References 48 publications

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“…For electric power grid, the counter dependent network can vary from water supply systems to natural gas networks and others. In interdependent electric power-water infrastructures, pump stations, control units, and storage tanks in water network are dependent on power supply from nearby electric substations (Wang et al, 2022). Meanwhile, several types of power plants, such as coal-fired power plants and nuclear power plants, depend on water supply for proper operation.…”

Section: Cascading Failure In General Interdependent Networkmentioning

confidence: 99%

Cyber-physical cascading failure and resilience of power grid: A comprehensive review

Islam

Lin²,

Vokkarane³

et al. 2023

Front. Energy Res.

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Smart grid technologies are based on the integration of the cyber network and the power grid into a cyber-physical power system (CPPS). The increasing cyber-physical interdependencies bring about tremendous opportunities for the modeling, monitoring, control, and protection of power grids, but also create new types of vulnerabilities and failure mechanisms threatening the reliability and resiliency of system operation. A major concern regarding the interdependent networks is the cascading failure (CF), where a small initial disturbance/failure in the network results in a seemingly unexpected large-scale failure. Although there has been a significant volume of recent work in the CF research of CPPS, a comprehensive review remains unavailable. This article aims to fill the gap by providing a systematic literature survey regarding the modeling, analysis, and mitigation of CF in CPPS. The open research questions for further research are also discussed. This article allows researchers to easily understand the state of the art of CF research in CPPS and fosters future work required towards full resolutions to the remaining questions and challenges.

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Section: Cascading Failure In General Interdependent Networkmentioning

confidence: 99%

Cyber-physical cascading failure and resilience of power grid: A comprehensive review

Islam

Lin²,

Vokkarane³

et al. 2023

Front. Energy Res.

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“…A more advanced set of integrated models, known as co-simulation models, adopt a distributed simulation approach. Co-simulation frameworks can be used to operate computationally intensive domain-specific submodels on different computers and seamlessly interact with each other on a real-time basis, enhancing both efficiency and usability [39]. Several frameworks and standards have been developed to implement co-simulation of complex systems, such as functional mock-up interface (FMI), high-level architecture (HLA), and distributed interactive simulation (DIS) [40].…”

Section: Integrated Infrastructure Simulation Modelsmentioning

confidence: 99%

“…Several frameworks and standards have been developed to implement co-simulation of complex systems, such as functional mock-up interface (FMI), high-level architecture (HLA), and distributed interactive simulation (DIS) [40]. In recent years, the highlevel architecture (HLA) standard has been increasingly adopted to develop interdependent infrastructure models for perform simulations in a distributed environment [41,39].…”

Section: Integrated Infrastructure Simulation Modelsmentioning

confidence: 99%

InfraRisk: An Open-Source Simulation Platform for Asset-Level Resilience Analysis in Interconnected Infrastructure Networks

Balakrishnan,

Cassottana

2022

Preprint

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Integrated simulation models are emerging as an alternative for analyzing large-scale interdependent infrastructure networks due to their modeling advantages over traditional interdependency models. This paper presents an open-source integrated simulation package for the asset-level analysis of interdependent infrastructure systems. The simulation platform, named 'InfraRisk' and developed in Python, can simulate disaster-induced infrastructure failures and subsequent post-disaster restoration in interconnected water-, power-, and road networks. InfraRisk consists of an infrastructure module, a hazard module, a recovery module, a simulation module, and a resilience quantification module. The infrastructure module integrates existing infrastructure network packages (wntr for water networks, pandapower for power systems, and a static traffic assignment model for transportation networks) through an interface that facilitates the network-level simulation of interdependent failures. The hazard module generates infrastructure component failure sequences based on various disaster characteristics. The recovery module determines repair sequences and assigns repair crews based on predefined heuristics-based recovery strategies or model predictive control (MPC) based optimization. Based on the schedule, the simulation module implements the network-wide simulation of the consequences of the disaster impacts and the recovery actions. The resilience quantification module offers system-level and consumer-level metrics to quantify

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“…Several models, such as, network theory-based models [5,6], system dynamics models [7,8], agent-based models [9,10] and input-output models [11,12], have been extensively used for modeling interdependent infrastructure systems and analyzing their resilience. More advanced computational models adopt co-simulation of multiple domain-specific infrastructure simulators instead of a homogeneous method to replicate the collective behavior of interdependent infrastructure systems [13].…”

Section: Introductionmentioning

confidence: 99%

Application of Clustering Algorithms for Dimensionality Reduction in Infrastructure Resilience Prediction Models

Balakrishnan¹,

Cassottana²,

Verma³

2022

Preprint

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Recent studies increasingly adopt simulation-based machine learning (ML) models to analyze critical infrastructure system resilience. For realistic applications, these ML models consider the component-level characteristics that influence the network response during emergencies. However, such an approach could result in a large number of features and cause ML models to suffer from the 'curse of dimensionality'. We present a clustering-based method that simultaneously minimizes the problem of high-dimensionality and improves the prediction accuracy of ML models developed for resilience analysis in large-scale interdependent infrastructure networks. The methodology has three parts: (a) generation of simulation dataset, (b) network component clustering, and (c) dimensionality reduction and development of prediction models. First, an interdependent infrastructure simulation model simulates the network-wide consequences of various disruptive events. The component-level features are extracted from the simulated data. Next, clustering algorithms

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Modeling cascading failure of interdependent critical infrastructure systems using HLA-based co-simulation

Cited by 20 publications

References 48 publications

Cyber-physical cascading failure and resilience of power grid: A comprehensive review

Cyber-physical cascading failure and resilience of power grid: A comprehensive review

InfraRisk: An Open-Source Simulation Platform for Asset-Level Resilience Analysis in Interconnected Infrastructure Networks

Application of Clustering Algorithms for Dimensionality Reduction in Infrastructure Resilience Prediction Models

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