470 research outputs found
Robust Decentralized State Estimation and Tracking for Power Systems via Network Gossiping
This paper proposes a fully decentralized adaptive re-weighted state
estimation (DARSE) scheme for power systems via network gossiping. The enabling
technique is the proposed Gossip-based Gauss-Newton (GGN) algorithm, which
allows to harness the computation capability of each area (i.e. a database
server that accrues data from local sensors) to collaboratively solve for an
accurate global state. The DARSE scheme mitigates the influence of bad data by
updating their error variances online and re-weighting their contributions
adaptively for state estimation. Thus, the global state can be estimated and
tracked robustly using near-neighbor communications in each area. Compared to
other distributed state estimation techniques, our communication model is
flexible with respect to reconfigurations and resilient to random failures as
long as the communication network is connected. Furthermore, we prove that the
Jacobian of the power flow equations satisfies the Lipschitz condition that is
essential for the GGN algorithm to converge to the desired solution.
Simulations of the IEEE-118 system show that the DARSE scheme can estimate and
track online the global power system state accurately, and degrades gracefully
when there are random failures and bad data.Comment: to appear in IEEE JSA
Gossip Algorithms for Distributed Signal Processing
Gossip algorithms are attractive for in-network processing in sensor networks
because they do not require any specialized routing, there is no bottleneck or
single point of failure, and they are robust to unreliable wireless network
conditions. Recently, there has been a surge of activity in the computer
science, control, signal processing, and information theory communities,
developing faster and more robust gossip algorithms and deriving theoretical
performance guarantees. This article presents an overview of recent work in the
area. We describe convergence rate results, which are related to the number of
transmitted messages and thus the amount of energy consumed in the network for
gossiping. We discuss issues related to gossiping over wireless links,
including the effects of quantization and noise, and we illustrate the use of
gossip algorithms for canonical signal processing tasks including distributed
estimation, source localization, and compression.Comment: Submitted to Proceedings of the IEEE, 29 page
Geographic Gossip: Efficient Averaging for Sensor Networks
Gossip algorithms for distributed computation are attractive due to their
simplicity, distributed nature, and robustness in noisy and uncertain
environments. However, using standard gossip algorithms can lead to a
significant waste in energy by repeatedly recirculating redundant information.
For realistic sensor network model topologies like grids and random geometric
graphs, the inefficiency of gossip schemes is related to the slow mixing times
of random walks on the communication graph. We propose and analyze an
alternative gossiping scheme that exploits geographic information. By utilizing
geographic routing combined with a simple resampling method, we demonstrate
substantial gains over previously proposed gossip protocols. For regular graphs
such as the ring or grid, our algorithm improves standard gossip by factors of
and respectively. For the more challenging case of random
geometric graphs, our algorithm computes the true average to accuracy
using radio
transmissions, which yields a factor improvement over
standard gossip algorithms. We illustrate these theoretical results with
experimental comparisons between our algorithm and standard methods as applied
to various classes of random fields.Comment: To appear, IEEE Transactions on Signal Processin
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