12 research outputs found
Can Distribution Grids Significantly Contribute to Transmission Grids' Voltage Management?
Power generation in Germany is currently transitioning from a system based on
large, central, thermal power plants to one that heavily relies on small,
decentral, mostly renewable power generators. This development poses the
question how transmission grids' reactive power demand for voltage management,
covered by central power plants today, can be supplied in the future.
In this work, we estimate the future technical potential of such an approach
for the whole of Germany. For a 100% renewable electricity scenario we set the
possible reactive power supply in comparison with the reactive power
requirements that are needed to realize the simulated future transmission grid
power flows. Since an exact calculation of distribution grids' reactive power
potential is difficult due to the unavailability of detailed grid models on
such scale, we optimistically estimate the potential by assuming a scaled,
averaged distribution grid model connected to each of the transmission grid
nodes.
We find that for all except a few transmission grid nodes, the required
reactive power can be fully supplied from the modeled distribution grids. This
implies that - even if our estimate is overly optimistic - distributed reactive
power provisioning will be a technical solution for many future reactive power
challenges
Distributed fuzzy decision making for production schedulling
In production systems, input materials (educts) pass through multiple
sequential stages until they become a product. The production stages
consist of different machines with various dynamic characteristics. The
coupling of those machines is a non-linear distributed system. With a
distributed control system based on a multi-agent approach, the produc-
tion system can achieve (almost) maximum output, where lot size and lot
sequence are the most important control variables. In most production
processes high throughput and low stock are conflicting goals. In order to
compare and compensate between these multiple goals, a fuzzy decision
making approach is employed here that decides about the material flow
and machine states, based on variables like working load or order queue
length
Quantum pattern recognition with liquid-state nuclear magnetic resonance
A novel quantum pattern recognition scheme is presented, which combines the
idea of a classic Hopfield neural network with adiabatic quantum computation.
Both the input and the memorized patterns are represented by means of the
problem Hamiltonian. In contrast to classic neural networks, the algorithm can
return a quantum superposition of multiple recognized patterns. A proof of
principle for the algorithm for two qubits is provided using a liquid state NMR
quantum computer.Comment: updated version, Journal-ref adde
Continuum percolation of wireless ad hoc communication networks
Wireless multi-hop ad hoc communication networks represent an
infrastructure-less and self-organized generalization of todays wireless
cellular networks. Connectivity within such a network is an important issue.
Continuum percolation and technology-driven mutations thereof allow to address
this issue in the static limit and to construct a simple distributed protocol,
guaranteeing strong connectivity almost surely and independently of various
typical uncorrelated and correlated random spatial patterns of participating ad
hoc nodes.Comment: 30 pages, to be published in Physica
Impact of network structure on the capacity of wireless multihop ad hoc communication
As a representative of a complex technological system, so-called wireless
multihop ad hoc communication networks are discussed. They represent an
infrastructure-less generalization of todays wireless cellular phone networks.
Lacking a central control authority, the ad hoc nodes have to coordinate
themselves such that the overall network performs in an optimal way. A
performance indicator is the end-to-end throughput capacity.
Various models, generating differing ad hoc network structure via differing
transmission power assignments, are constructed and characterized. They serve
as input for a generic data traffic simulation as well as some semi-analytic
estimations. The latter reveal that due to the most-critical-node effect the
end-to-end throughput capacity sensitively depends on the underlying network
structure, resulting in differing scaling laws with respect to network size.Comment: 30 pages, to be published in Physica
Distributed fuzzy decision making for production schedulling
In production systems, input materials (educts) pass through multiple
sequential stages until they become a product. The production stages
consist of different machines with various dynamic characteristics. The
coupling of those machines is a non-linear distributed system. With a
distributed control system based on a multi-agent approach, the produc-
tion system can achieve (almost) maximum output, where lot size and lot
sequence are the most important control variables. In most production
processes high throughput and low stock are conflicting goals. In order to
compare and compensate between these multiple goals, a fuzzy decision
making approach is employed here that decides about the material flow
and machine states, based on variables like working load or order queue
length
Distributed fuzzy decision making for production schedulling
In production systems, input materials (educts) pass through multiple
sequential stages until they become a product. The production stages
consist of different machines with various dynamic characteristics. The
coupling of those machines is a non-linear distributed system. With a
distributed control system based on a multi-agent approach, the produc-
tion system can achieve (almost) maximum output, where lot size and lot
sequence are the most important control variables. In most production
processes high throughput and low stock are conflicting goals. In order to
compare and compensate between these multiple goals, a fuzzy decision
making approach is employed here that decides about the material flow
and machine states, based on variables like working load or order queue
length
Spiral Recurrent Neural Network for Online Learning
Abstract. Autonomous, self * sensor networks require sensor nodes with a certain degree of “intelligence”. An elementary component of such an “intelligence ” is the ability to learn online predicting sensor values. We consider recurrent neural network (RNN) models trained with an extended Kalman filter algorithm based on real time recurrent learning (RTRL) with teacher forcing. We compared the performance of conventional neural network architectures with that of spiral recurrent neural networks (Spiral RNN)- a novel RNN architecture combining a trainable hidden recurrent layer with the “echo state ” property of echo state neural networks (ESN). We found that this novel RNN architecture shows more stable performance and faster convergence.