179,234 research outputs found
Failure Localization in Power Systems via Tree Partitions
Cascading failures in power systems propagate non-locally, making the control
and mitigation of outages extremely hard. In this work, we use the emerging
concept of the tree partition of transmission networks to provide an analytical
characterization of line failure localizability in transmission systems. Our
results rigorously establish the well perceived intuition in power community
that failures cannot cross bridges, and reveal a finer-grained concept that
encodes more precise information on failure propagations within tree-partition
regions. Specifically, when a non-bridge line is tripped, the impact of this
failure only propagates within well-defined components, which we refer to as
cells, of the tree partition defined by the bridges. In contrast, when a bridge
line is tripped, the impact of this failure propagates globally across the
network, affecting the power flow on all remaining transmission lines. This
characterization suggests that it is possible to improve the system robustness
by temporarily switching off certain transmission lines, so as to create more,
smaller components in the tree partition; thus spatially localizing line
failures and making the grid less vulnerable to large-scale outages. We
illustrate this approach using the IEEE 118-bus test system and demonstrate
that switching off a negligible portion of transmission lines allows the impact
of line failures to be significantly more localized without substantial changes
in line congestion
Graphical Models for Optimal Power Flow
Optimal power flow (OPF) is the central optimization problem in electric
power grids. Although solved routinely in the course of power grid operations,
it is known to be strongly NP-hard in general, and weakly NP-hard over tree
networks. In this paper, we formulate the optimal power flow problem over tree
networks as an inference problem over a tree-structured graphical model where
the nodal variables are low-dimensional vectors. We adapt the standard dynamic
programming algorithm for inference over a tree-structured graphical model to
the OPF problem. Combining this with an interval discretization of the nodal
variables, we develop an approximation algorithm for the OPF problem. Further,
we use techniques from constraint programming (CP) to perform interval
computations and adaptive bound propagation to obtain practically efficient
algorithms. Compared to previous algorithms that solve OPF with optimality
guarantees using convex relaxations, our approach is able to work for arbitrary
distribution networks and handle mixed-integer optimization problems. Further,
it can be implemented in a distributed message-passing fashion that is scalable
and is suitable for "smart grid" applications like control of distributed
energy resources. We evaluate our technique numerically on several benchmark
networks and show that practical OPF problems can be solved effectively using
this approach.Comment: To appear in Proceedings of the 22nd International Conference on
Principles and Practice of Constraint Programming (CP 2016
Information flow and cooperative control of vehicle formations
We consider the problem of cooperation among a collection of vehicles performing a shared task using intervehicle communication to coordinate their actions. Tools from algebraic graph theory prove useful in modeling the communication network and relating its topology to formation stability. We prove a Nyquist criterion that uses the eigenvalues of the graph Laplacian matrix to determine the effect of the communication topology on formation stability. We also propose a method for decentralized information exchange between vehicles. This approach realizes a dynamical system that supplies each vehicle with a common reference to be used for cooperative motion. We prove a separation principle that decomposes formation stability into two components: Stability of this is achieved information flow for the given graph and stability of an individual vehicle for the given controller. The information flow can thus be rendered highly robust to changes in the graph, enabling tight formation control despite limitations in intervehicle communication capability
Integrated Inference and Learning of Neural Factors in Structural Support Vector Machines
Tackling pattern recognition problems in areas such as computer vision,
bioinformatics, speech or text recognition is often done best by taking into
account task-specific statistical relations between output variables. In
structured prediction, this internal structure is used to predict multiple
outputs simultaneously, leading to more accurate and coherent predictions.
Structural support vector machines (SSVMs) are nonprobabilistic models that
optimize a joint input-output function through margin-based learning. Because
SSVMs generally disregard the interplay between unary and interaction factors
during the training phase, final parameters are suboptimal. Moreover, its
factors are often restricted to linear combinations of input features, limiting
its generalization power. To improve prediction accuracy, this paper proposes:
(i) Joint inference and learning by integration of back-propagation and
loss-augmented inference in SSVM subgradient descent; (ii) Extending SSVM
factors to neural networks that form highly nonlinear functions of input
features. Image segmentation benchmark results demonstrate improvements over
conventional SSVM training methods in terms of accuracy, highlighting the
feasibility of end-to-end SSVM training with neural factors
Energy flow polynomials: A complete linear basis for jet substructure
We introduce the energy flow polynomials: a complete set of jet substructure
observables which form a discrete linear basis for all infrared- and
collinear-safe observables. Energy flow polynomials are multiparticle energy
correlators with specific angular structures that are a direct consequence of
infrared and collinear safety. We establish a powerful graph-theoretic
representation of the energy flow polynomials which allows us to design
efficient algorithms for their computation. Many common jet observables are
exact linear combinations of energy flow polynomials, and we demonstrate the
linear spanning nature of the energy flow basis by performing regression for
several common jet observables. Using linear classification with energy flow
polynomials, we achieve excellent performance on three representative jet
tagging problems: quark/gluon discrimination, boosted W tagging, and boosted
top tagging. The energy flow basis provides a systematic framework for complete
investigations of jet substructure using linear methods.Comment: 41+15 pages, 13 figures, 5 tables; v2: updated to match JHEP versio
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