6 research outputs found
Statistical Classification of Cascading Failures in Power Grids
We introduce a new microscopic model of the outages in transmission power
grids. This model accounts for the automatic response of the grid to load
fluctuations that take place on the scale of minutes, when the optimum power
flow adjustments and load shedding controls are unavailable. We describe
extreme events, initiated by load fluctuations, which cause cascading failures
of loads, generators and lines. Our model is quasi-static in the causal,
discrete time and sequential resolution of individual failures. The model, in
its simplest realization based on the Directed Current description of the power
flow problem, is tested on three standard IEEE systems consisting of 30, 39 and
118 buses. Our statistical analysis suggests a straightforward classification
of cascading and islanding phases in terms of the ratios between average number
of removed loads, generators and links. The analysis also demonstrates
sensitivity to variations in line capacities. Future research challenges in
modeling and control of cascading outages over real-world power networks are
discussed.Comment: 8 pages, 8 figure
Hierarchical Consensus Formation Reduces the Influence of Opinion Bias
We study the role of hierarchical structures in a simple model of collective
consensus formation based on the bounded confidence model with continuous
individual opinions. For the particular variation of this model considered in
this paper, we assume that a bias towards an extreme opinion is introduced
whenever two individuals interact and form a common decision. As a simple proxy
for hierarchical social structures, we introduce a two-step decision making
process in which in the second step groups of like-minded individuals are
replaced by representatives once they have reached local consensus, and the
representatives in turn form a collective decision in a downstream process. We
find that the introduction of such a hierarchical decision making structure can
improve consensus formation, in the sense that the eventual collective opinion
is closer to the true average of individual opinions than without it. In
particular, we numerically study how the size of groups of like-minded
individuals being represented by delegate individuals affects the impact of the
bias on the final population-wide consensus. These results are of interest for
the design of organisational policies and the optimisation of hierarchical
structures in the context of group decision making.Comment: 12 pages, 5 figure
Betweenness Preference: Quantifying Correlations in the Topological Dynamics of Temporal Networks
We study correlations in temporal networks and introduce the notion of
betweenness preference. It allows to quantify to what extent paths, existing in
time-aggregated representations of temporal networks, are actually realizable
based on the sequence of interactions. We show that betweenness preference is
present in empirical temporal network data and that it influences the length of
shortest time-respecting paths. Using four different data sets, we further
argue that neglecting betweenness preference leads to wrong conclusions about
dynamical processes on temporal networks.Comment: 10 pages, 4 figure