3,519 research outputs found
Impact Assessment of Hypothesized Cyberattacks on Interconnected Bulk Power Systems
The first-ever Ukraine cyberattack on power grid has proven its devastation
by hacking into their critical cyber assets. With administrative privileges
accessing substation networks/local control centers, one intelligent way of
coordinated cyberattacks is to execute a series of disruptive switching
executions on multiple substations using compromised supervisory control and
data acquisition (SCADA) systems. These actions can cause significant impacts
to an interconnected power grid. Unlike the previous power blackouts, such
high-impact initiating events can aggravate operating conditions, initiating
instability that may lead to system-wide cascading failure. A systemic
evaluation of "nightmare" scenarios is highly desirable for asset owners to
manage and prioritize the maintenance and investment in protecting their
cyberinfrastructure. This survey paper is a conceptual expansion of real-time
monitoring, anomaly detection, impact analyses, and mitigation (RAIM) framework
that emphasizes on the resulting impacts, both on steady-state and dynamic
aspects of power system stability. Hypothetically, we associate the
combinatorial analyses of steady state on substations/components outages and
dynamics of the sequential switching orders as part of the permutation. The
expanded framework includes (1) critical/noncritical combination verification,
(2) cascade confirmation, and (3) combination re-evaluation. This paper ends
with a discussion of the open issues for metrics and future design pertaining
the impact quantification of cyber-related contingencies
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
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
Topological analysis of the power grid and mitigation strategies against cascading failures
This paper presents a complex systems overview of a power grid network. In
recent years, concerns about the robustness of the power grid have grown
because of several cascading outages in different parts of the world. In this
paper, cascading effect has been simulated on three different networks, the
IEEE 300 bus test system, the IEEE 118 bus test system, and the WSCC 179 bus
equivalent model, using the DC Power Flow Model. Power Degradation has been
discussed as a measure to estimate the damage to the network, in terms of load
loss and node loss. A network generator has been developed to generate graphs
with characteristics similar to the IEEE standard networks and the generated
graphs are then compared with the standard networks to show the effect of
topology in determining the robustness of a power grid. Three mitigation
strategies, Homogeneous Load Reduction, Targeted Range-Based Load Reduction,
and Use of Distributed Renewable Sources in combination with Islanding, have
been suggested. The Homogeneous Load Reduction is the simplest to implement but
the Targeted Range-Based Load Reduction is the most effective strategy.Comment: 5 pages, 8 figures, 1 table. This is a limited version of the work
due to space limitations of the conference paper. A detailed version is
submitted to the IEEE Systems Journal and is currently under revie
Structural Vulnerability Analysis of Electric Power Distribution Grids
Power grid outages cause huge economical and societal costs. Disruptions in
the power distribution grid are responsible for a significant fraction of
electric power unavailability to customers. The impact of extreme weather
conditions, continuously increasing demand, and the over-ageing of assets in
the grid, deteriorates the safety of electric power delivery in the near
future. It is this dependence on electric power that necessitates further
research in the power distribution grid security assessment. Thus measures to
analyze the robustness characteristics and to identify vulnerabilities as they
exist in the grid are of utmost importance. This research investigates exactly
those concepts- the vulnerability and robustness of power distribution grids
from a topological point of view, and proposes a metric to quantify them with
respect to assets in a distribution grid. Real-world data is used to
demonstrate the applicability of the proposed metric as a tool to assess the
criticality of assets in a distribution grid
Modelling and vulnerability analysis of cyber-physical power systems based on interdependent networks
The strong coupling between the power grid and communication systems may contribute to failure propagation, which may easily lead to cascading failures or blackouts. In this paper, in order to quantitatively analyse the impact of interdependency on power system vulnerability, we put forward a “degree–electrical degree” independent model of cyber-physical power systems (CPPS), a new type of assortative link, through identifying the important nodes in a power grid based on the proposed index–electrical degree, and coupling them with the nodes in a communication system with a high degree, based on one-to-one correspondence. Using the double-star communication system and the IEEE 118-bus power grid to form an artificial interdependent network, we evaluated and compare the holistic vulnerability of CPPS under random attack and malicious attack, separately based on three kinds of interdependent models: “degree–betweenness”, “degree–electrical degree” and “random link”. The simulation results demonstrated that different link patterns, coupling degrees and attack types all can influence the vulnerability of CPPS. The CPPS with a “degree–electrical degree” interdependent model proposed in this paper presented a higher robustness in the face of random attack, and moreover performed better than the degree–betweenness interdependent model in the face of malicious attack
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