8,937 research outputs found
A simple spatiotemporal evolution model of a transmission power grid
In this paper, we present a model for the spatial and temporal evolution of a particularly large human-made network: the 400-kV French transmission power grid. This is based on 1) an attachment procedure that diminishes the connection probability between two nodes as the network grows and 2) a coupled cost function characterizing the available budget at every time step. Two differentiated and consecutive processes can be distinguished: a first global space-filling process and a secondary local meshing process that increases connectivity at a local level. Results show that even without power system engineering design constraints (i.e., population and energy demand), the evolution of a transmission network can be remarkably explained by means of a simple attachment procedure. Given a distribution of resources and a time span, the model can also be used to generate the probability distribution of cable lengths at every time step, thus facilitating network planning. Implications for network's fragility are suggested as a starting point for new design perspectives in this kind of infrastructures.Peer ReviewedPostprint (author's final draft
Load curve data cleansing and imputation via sparsity and low rank
The smart grid vision is to build an intelligent power network with an
unprecedented level of situational awareness and controllability over its
services and infrastructure. This paper advocates statistical inference methods
to robustify power monitoring tasks against the outlier effects owing to faulty
readings and malicious attacks, as well as against missing data due to privacy
concerns and communication errors. In this context, a novel load cleansing and
imputation scheme is developed leveraging the low intrinsic-dimensionality of
spatiotemporal load profiles and the sparse nature of "bad data.'' A robust
estimator based on principal components pursuit (PCP) is adopted, which effects
a twofold sparsity-promoting regularization through an -norm of the
outliers, and the nuclear norm of the nominal load profiles. Upon recasting the
non-separable nuclear norm into a form amenable to decentralized optimization,
a distributed (D-) PCP algorithm is developed to carry out the imputation and
cleansing tasks using networked devices comprising the so-termed advanced
metering infrastructure. If D-PCP converges and a qualification inequality is
satisfied, the novel distributed estimator provably attains the performance of
its centralized PCP counterpart, which has access to all networkwide data.
Computer simulations and tests with real load curve data corroborate the
convergence and effectiveness of the novel D-PCP algorithm.Comment: 8 figures, submitted to IEEE Transactions on Smart Grid - Special
issue on "Optimization methods and algorithms applied to smart grid
Asynchronous Networks and Event Driven Dynamics
Real-world networks in technology, engineering and biology often exhibit
dynamics that cannot be adequately reproduced using network models given by
smooth dynamical systems and a fixed network topology. Asynchronous networks
give a theoretical and conceptual framework for the study of network dynamics
where nodes can evolve independently of one another, be constrained, stop, and
later restart, and where the interaction between different components of the
network may depend on time, state, and stochastic effects. This framework is
sufficiently general to encompass a wide range of applications ranging from
engineering to neuroscience. Typically, dynamics is piecewise smooth and there
are relationships with Filippov systems. In the first part of the paper, we
give examples of asynchronous networks, and describe the basic formalism and
structure. In the second part, we make the notion of a functional asynchronous
network rigorous, discuss the phenomenon of dynamical locks, and present a
foundational result on the spatiotemporal factorization of the dynamics for a
large class of functional asynchronous networks
Numerical approaches to time evolution of complex quantum systems
We examine several numerical techniques for the calculation of the dynamics
of quantum systems. In particular, we single out an iterative method which is
based on expanding the time evolution operator into a finite series of
Chebyshev polynomials. The Chebyshev approach benefits from two advantages over
the standard time-integration Crank-Nicholson scheme: speedup and efficiency.
Potential competitors are semiclassical methods such as the Wigner-Moyal or
quantum tomographic approaches. We outline the basic concepts of these
techniques and benchmark their performance against the Chebyshev approach by
monitoring the time evolution of a Gaussian wave packet in restricted
one-dimensional (1D) geometries. Thereby the focus is on tunnelling processes
and the motion in anharmonic potentials. Finally we apply the prominent
Chebyshev technique to two highly non-trivial problems of current interest: (i)
the injection of a particle in a disordered 2D graphene nanoribbon and (ii) the
spatiotemporal evolution of polaron states in finite quantum systems. Here,
depending on the disorder/electron-phonon coupling strength and the device
dimensions, we observe transmission or localisation of the matter wave.Comment: 8 pages, 3 figure
Spiral-wave dynamics in a mathematical model of human ventricular tissue with myocytes and Purkinje fibers
We present systematic numerical studies of the possible effects of the coupling of human endocardial and Purkinje cells at cellular and two-dimensional tissue levels. We find that the autorhythmic-activity frequency of the Purkinje cell in a composite decreases with an increase in the coupling strength; this can even eliminate the autorhythmicity. We observe a delay between the beginning of the action potentials of endocardial and Purkinje cells in a composite; such a delay increases as we decrease the diffusive coupling, and eventually a failure of transmission occurs. An increase in the diffusive coupling decreases the slope of the action-potential-duration-restitution curve of an endocardial cell in a composite. By using a minimal model for the Purkinje network, in which we have a two-dimensional, bilayer tissue, with a layer of Purkinje cells on top of a layer of endocardial cells, we can stabilize spiral-wave turbulence; however, for a sparse distribution of Purkinje-ventricular junctions, at which these two layers are coupled, we can also obtain additional focal activity and many complex transient regimes. We also present additional effects resulting from the coupling of Purkinje and endocardial layers and discuss the relation of our results to the studies performed in anatomically accurate models of the Purkinje network
Applied Koopman Operator Theory for Power Systems Technology
Koopman operator is a composition operator defined for a dynamical system
described by nonlinear differential or difference equation. Although the
original system is nonlinear and evolves on a finite-dimensional state space,
the Koopman operator itself is linear but infinite-dimensional (evolves on a
function space). This linear operator captures the full information of the
dynamics described by the original nonlinear system. In particular, spectral
properties of the Koopman operator play a crucial role in analyzing the
original system. In the first part of this paper, we review the so-called
Koopman operator theory for nonlinear dynamical systems, with emphasis on modal
decomposition and computation that are direct to wide applications. Then, in
the second part, we present a series of applications of the Koopman operator
theory to power systems technology. The applications are established as
data-centric methods, namely, how to use massive quantities of data obtained
numerically and experimentally, through spectral analysis of the Koopman
operator: coherency identification of swings in coupled synchronous generators,
precursor diagnostic of instabilities in the coupled swing dynamics, and
stability assessment of power systems without any use of mathematical models.
Future problems of this research direction are identified in the last
concluding part of this paper.Comment: 31 pages, 11 figure
Optimizing Associative Information Transfer within Content-addressable Memory
Original article can be found at: http://www.oldcitypublishing.com/IJUC/IJUC.htmlPeer reviewe
- …