Consensus and Clustering of Expressed and Private Opinions in Dynamical Networks Against Attacks

Shang, Yilun

doi:10.1109/jsyst.2019.2956116

Cited by 17 publications

(6 citation statements)

References 26 publications

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“…Opinion dynamics have been studied for about 50 years, but it has attracted a lot of attention relatively recently [123,124]. In the past few years, opinion dynamics are being used against attacks on networks [125][126][127]. Therefore, combining machine learning and opinion dynamics has the potential to be used and applied to power grids against cyber-attacks as well.…”

Section: Discussionmentioning

confidence: 99%

“…Opinion dynamics have been studied for about 50 years; however, they have attracted a lot of attention recently [ 164 , 165 ]. In the past few years, opinion dynamics are being used to thwart attacks on networks [ 166 , 167 , 168 ]. Therefore, combining ML and opinion dynamics is a potential future technique that could be applied to power grids to combat cyberattacks.…”

Section: Future Work and Path Forwardmentioning

confidence: 99%

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Data‐driven operation of the resilient electric grid: A case of COVID‐19

Noorazar

Srivastava

Pannala

et al. 2021

The Journal of Engineering

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Electrical energy is a vital part of modern life, and expectations for grid resilience to allow a continuous and reliable energy supply has tremendously increased even during adverse events (e.g. Ukraine cyberattack, Hurricane Maria). The global pandemic COVID‐19 has raised the electric energy reliability risk due to potential workforce disruptions, supply chain interruptions, and increased possible cybersecurity threats. Additionally, the pandemic introduces a significant degree of uncertainty to the grid operation in the presence of other challenges including aging power grids, high proliferation of distributed generation, market mechanism, and active distribution network. This situation increases the need for measures for the resiliency of power grids to mitigate the impact of the pandemic as well as simultaneous extreme events including cyberattacks and adverse weather events. Solutions to manage such an adverse scenario will be multi‐fold: (a) emergency planning and organisational support, (b) following safety protocol, (c) utilising enhanced automation and sensing for situational awareness, and (d) integration of advanced technologies and data points for ML‐driven enhanced decision support. Enhanced digitalisation and automation resulted in better network visibility at various levels, including generation, transmission, and distribution. These data or information can be employed to take advantage of advanced machine learning techniques for automation and increased power grid resilience. In this paper, the resilience of power grids in the face of pandemics is explored and various machine learning tools that can be helpful to augment human operators are discused by: (a) reviewing the impact of COVID‐19 on power grid operations and actions taken by operators/organisations to minimise the impact of COVID‐19, and (b) presenting recently developed tools and concepts of machine learning and artificial intelligence that can be applied to increase the resiliency of power systems in normal and extreme scenarios such as the COVID‐19 pandemic.

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Section: Discussionmentioning

confidence: 99%

Section: Future Work and Path Forwardmentioning

confidence: 99%

Data‐driven operation of the resilient electric grid: A case of COVID‐19

Noorazar

Srivastava

Pannala

et al. 2021

The Journal of Engineering

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“…Complex networks take the networks topology and the dynamic models of networks nodes as the main research object. With the help of mathematical science and information science, complex networks are widely applied in neural networks, social networks, control theory, and consensus [1][2][3][4]. The problem of networks consensus means that networks reach a common decision on a certain issue.…”

Section: Introductionmentioning

confidence: 99%

Coherence Analysis of Symmetric Star Topology Networks

Gao¹,

Zhu

Chen

et al. 2022

Front. Phys.

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The dynamics of complex networks are closely related to the topological structure. As an important research branch, the problem of network consensus has attracted more attention. In this paper, the first-order coherence of three kinds of symmetric star topology networks are studied by using the theory of network science. Firstly, three kinds of symmetric star topology network models are given. Secondly, the first-order coherence of these networks are calculated by using matrix theory. The relationships among the first-order coherence of the network and branch length and the number of branches change are obtained by numerical simulation. Finally, we found that the third network has the best consensus, and the change of branch length has more effective impact on network consensus.

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“…The regulation protocols have been mainly designed in the control theory community and do not take into consideration of substantive social interactions or neighbor dependent synergy. Several recent work [15][16][17] has scoped the difference between expressed and private opinions in social networks, but they again do not directly speak to the observer effect in social dynamics.…”

Section: Introductionmentioning

confidence: 99%

Consensus and Clustering of Expressed and Private Opinions in Dynamical Networks Against Attacks

Cited by 17 publications

References 26 publications

Data‐driven operation of the resilient electric grid: A case of COVID‐19

Data‐driven operation of the resilient electric grid: A case of COVID‐19

Coherence Analysis of Symmetric Star Topology Networks

Consensus formation in networks with neighbor-dependent synergy and observer effect

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