5,685 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
Analyzing Cascading Failures in Smart Grids under Random and Targeted Attacks
We model smart grids as complex interdependent networks, and study targeted
attacks on smart grids for the first time. A smart grid consists of two
networks: the power network and the communication network, interconnected by
edges. Occurrence of failures (attacks) in one network triggers failures in the
other network, and propagates in cascades across the networks. Such cascading
failures can result in disintegration of either (or both) of the networks.
Earlier works considered only random failures. In practical situations, an
attacker is more likely to compromise nodes selectively.
We study cascading failures in smart grids, where an attacker selectively
compromises the nodes with probabilities proportional to their degrees; high
degree nodes are compromised with higher probability. We mathematically analyze
the sizes of the giant components of the networks under targeted attacks, and
compare the results with the corresponding sizes under random attacks. We show
that networks disintegrate faster for targeted attacks compared to random
attacks. A targeted attack on a small fraction of high degree nodes
disintegrates one or both of the networks, whereas both the networks contain
giant components for random attack on the same fraction of nodes.Comment: Accepted for publication in 28th IEEE International Conference on
Advanced Information Networking and Applications (AINA) 201
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Evaluating the resilience and security of boundaryless, evolving socio-technical Systems of Systems
Modelling interdependencies between the electricity and information infrastructures
The aim of this paper is to provide qualitative models characterizing
interdependencies related failures of two critical infrastructures: the
electricity infrastructure and the associated information infrastructure. The
interdependencies of these two infrastructures are increasing due to a growing
connection of the power grid networks to the global information infrastructure,
as a consequence of market deregulation and opening. These interdependencies
increase the risk of failures. We focus on cascading, escalating and
common-cause failures, which correspond to the main causes of failures due to
interdependencies. We address failures in the electricity infrastructure, in
combination with accidental failures in the information infrastructure, then we
show briefly how malicious attacks in the information infrastructure can be
addressed
The xSAP Safety Analysis Platform
This paper describes the xSAP safety analysis platform. xSAP provides several
model-based safety analysis features for finite- and infinite-state synchronous
transition systems. In particular, it supports library-based definition of
fault modes, an automatic model extension facility, generation of safety
analysis artifacts such as Dynamic Fault Trees (DFTs) and Failure Mode and
Effects Analysis (FMEA) tables. Moreover, it supports probabilistic evaluation
of Fault Trees, failure propagation analysis using Timed Failure Propagation
Graphs (TFPGs), and Common Cause Analysis (CCA). xSAP has been used in several
industrial projects as verification back-end, and is currently being evaluated
in a joint R&D Project involving FBK and The Boeing Company
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