84,374 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
Large Scale In Silico Screening on Grid Infrastructures
Large-scale grid infrastructures for in silico drug discovery open
opportunities of particular interest to neglected and emerging diseases. In
2005 and 2006, we have been able to deploy large scale in silico docking within
the framework of the WISDOM initiative against Malaria and Avian Flu requiring
about 105 years of CPU on the EGEE, Auvergrid and TWGrid infrastructures. These
achievements demonstrated the relevance of large-scale grid infrastructures for
the virtual screening by molecular docking. This also allowed evaluating the
performances of the grid infrastructures and to identify specific issues raised
by large-scale deployment.Comment: 14 pages, 2 figures, 2 tables, The Third International Life Science
Grid Workshop, LSGrid 2006, Yokohama, Japan, 13-14 october 2006, to appear in
the proceeding
Discovering Job Preemptions in the Open Science Grid
The Open Science Grid(OSG) is a world-wide computing system which facilitates
distributed computing for scientific research. It can distribute a
computationally intensive job to geo-distributed clusters and process job's
tasks in parallel. For compute clusters on the OSG, physical resources may be
shared between OSG and cluster's local user-submitted jobs, with local jobs
preempting OSG-based ones. As a result, job preemptions occur frequently in
OSG, sometimes significantly delaying job completion time.
We have collected job data from OSG over a period of more than 80 days. We
present an analysis of the data, characterizing the preemption patterns and
different types of jobs. Based on observations, we have grouped OSG jobs into 5
categories and analyze the runtime statistics for each category. we further
choose different statistical distributions to estimate probability density
function of job runtime for different classes.Comment: 8 page
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