13,157 research outputs found
Modeling Fault Propagation Paths in Power Systems: A New Framework Based on Event SNP Systems With Neurotransmitter Concentration
To reveal fault propagation paths is one of the most critical studies for the analysis of
power system security; however, it is rather dif cult. This paper proposes a new framework for the fault
propagation path modeling method of power systems based on membrane computing.We rst model the fault
propagation paths by proposing the event spiking neural P systems (Ev-SNP systems) with neurotransmitter
concentration, which can intuitively reveal the fault propagation path due to the ability of its graphics models
and parallel knowledge reasoning. The neurotransmitter concentration is used to represent the probability
and gravity degree of fault propagation among synapses. Then, to reduce the dimension of the Ev-SNP
system and make them suitable for large-scale power systems, we propose a model reduction method
for the Ev-SNP system and devise its simpli ed model by constructing single-input and single-output
neurons, called reduction-SNP system (RSNP system). Moreover, we apply the RSNP system to the IEEE
14- and 118-bus systems to study their fault propagation paths. The proposed approach rst extends the
SNP systems to a large-scaled application in critical infrastructures from a single element to a system-wise
investigation as well as from the post-ante fault diagnosis to a new ex-ante fault propagation path prediction,
and the simulation results show a new success and promising approach to the engineering domain
Adjacent Graph Based Vulnerability Assessment for Electrical Networks Considering Fault Adjacent Relationships Among Branches
Security issues related to vulnerability assessment in electrical networks are necessary for operators to identify the critical branches. At present, using complex network theory to assess the structural vulnerability of the electrical network is a popular method. However, the complex network theory cannot be comprehensively applicable to the operational vulnerability assessment of the electrical network because the network operation is closely dependent on the physical rules not only on the topological structure. To overcome the problem, an adjacent graph (AG) considering the topological, physical, and operational features of the electrical network is constructed to replace the original network. Through the AG, a branch importance index that considers both the importance of a branch and the fault adjacent relationships among branches is constructed to evaluate the electrical network vulnerability. The IEEE 118-bus system and the French grid are employed to validate the effectiveness of the proposed method.National Natural Science Foundation of China under Grant U1734202National Key Research and Development Plan of China under Grant 2017YFB1200802-12National Natural Science Foundation of China under Grant 51877181National Natural Science Foundation of China under Grant 61703345Chinese Academy of Sciences, under Grant 2018-2019-0
Evolution of Threats in the Global Risk Network
With a steadily growing population and rapid advancements in technology, the
global economy is increasing in size and complexity. This growth exacerbates
global vulnerabilities and may lead to unforeseen consequences such as global
pandemics fueled by air travel, cyberspace attacks, and cascading failures
caused by the weakest link in a supply chain. Hence, a quantitative
understanding of the mechanisms driving global network vulnerabilities is
urgently needed. Developing methods for efficiently monitoring evolution of the
global economy is essential to such understanding. Each year the World Economic
Forum publishes an authoritative report on the state of the global economy and
identifies risks that are likely to be active, impactful or contagious. Using a
Cascading Alternating Renewal Process approach to model the dynamics of the
global risk network, we are able to answer critical questions regarding the
evolution of this network. To fully trace the evolution of the network we
analyze the asymptotic state of risks (risk levels which would be reached in
the long term if the risks were left unabated) given a snapshot in time, this
elucidates the various challenges faced by the world community at each point in
time. We also investigate the influence exerted by each risk on others. Results
presented here are obtained through either quantitative analysis or
computational simulations.Comment: 27 pages, 15 figure
Methodology for Designing Decision Support Systems for Visualising and Mitigating Supply Chain Cyber Risk from IoT Technologies
This paper proposes a methodology for designing decision support systems for
visualising and mitigating the Internet of Things cyber risks. Digital
technologies present new cyber risk in the supply chain which are often not
visible to companies participating in the supply chains. This study
investigates how the Internet of Things cyber risks can be visualised and
mitigated in the process of designing business and supply chain strategies. The
emerging DSS methodology present new findings on how digital technologies
affect business and supply chain systems. Through epistemological analysis, the
article derives with a decision support system for visualising supply chain
cyber risk from Internet of Things digital technologies. Such methods do not
exist at present and this represents the first attempt to devise a decision
support system that would enable practitioners to develop a step by step
process for visualising, assessing and mitigating the emerging cyber risk from
IoT technologies on shared infrastructure in legacy supply chain systems
Ten-tier and multi-scale supplychain network analysis of medical equipment: Random failure and intelligent attack analysis
Motivated by the COVID-19 pandemic, this paper explores the supply chain
viability of medical equipment, an industry whose supply chain was put under a
crucial test during the pandemic. This paper includes an empirical
network-level analysis of supplier reachability under Random Failure Experiment
(RFE) and Intelligent Attack Experiment (IAE). Specifically, this study
investigates the effect of RFA and IAE across multiple tiers and scales. The
global supply chain data was mined and analyzed from about 45,000 firms with
about 115,000 intertwined relationships spanning across 10 tiers of the
backward supply chain of medical equipment. This complex supply chain network
was analyzed at four scales, namely: firm, country-industry, industry, and
country. A notable contribution of this study is the application of a supply
chain tier optimization tool to identify the lowest tier of the supply chain
that can provide adequate resolution for the study of the supply chain pattern.
We also developed data-driven-tools to identify the thresholds for breakdown
and fragmentation of the medical equipment supply chain when faced with random
failures or different intelligent attack scenarios. The novel network analysis
tools utilized in the study can be applied to the study of supply chain
reachability and viability in other industries.Comment: 47 page
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