17,917 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
A Framework for Phasor Measurement Placement in Hybrid State Estimation via Gauss-Newton
In this paper, we study the placement of Phasor Measurement Units (PMU) for
enhancing hybrid state estimation via the traditional Gauss-Newton method,
which uses measurements from both PMU devices and Supervisory Control and Data
Acquisition (SCADA) systems. To compare the impact of PMU placements, we
introduce a useful metric which accounts for three important requirements in
power system state estimation: {\it convergence}, {\it observability} and {\it
performance} (COP). Our COP metric can be used to evaluate the estimation
performance and numerical stability of the state estimator, which is later used
to optimize the PMU locations. In particular, we cast the optimal placement
problem in a unified formulation as a semi-definite program (SDP) with integer
variables and constraints that guarantee observability in case of measurements
loss. Last but not least, we propose a relaxation scheme of the original
integer-constrained SDP with randomization techniques, which closely
approximates the optimum deployment. Simulations of the IEEE-30 and 118 systems
corroborate our analysis, showing that the proposed scheme improves the
convergence of the state estimator, while maintaining optimal asymptotic
performance.Comment: accepted to IEEE Trans. on Power System
The Calabi-Yau equation on 4-manifolds over 2-tori
This paper pursues the study of the Calabi-Yau equation on certain symplectic
non-Kaehler 4-manifolds, building on a key example of Tosatti-Weinkove in which
more general theory had proved less effective. Symplectic 4-manifolds admitting
a 2-torus fibration over a 2-torus base are modelled on one of three solvable
Lie groups. Having assigned an invariant almost-Kaehler structure and a volume
form that effectively varies only on the base, one seeks a symplectic form with
this volume. Our approach simplifies the previous analysis of the problem, and
establishes the existence of solutions in various other cases.Comment: 24 page
Approximate Sparse Recovery: Optimizing Time and Measurements
An approximate sparse recovery system consists of parameters , an
-by- measurement matrix, , and a decoding algorithm, .
Given a vector, , the system approximates by , which must satisfy , where denotes the optimal -term approximation to . For
each vector , the system must succeed with probability at least 3/4. Among
the goals in designing such systems are minimizing the number of
measurements and the runtime of the decoding algorithm, .
In this paper, we give a system with
measurements--matching a lower bound, up to a constant factor--and decoding
time , matching a lower bound up to factors.
We also consider the encode time (i.e., the time to multiply by ),
the time to update measurements (i.e., the time to multiply by a
1-sparse ), and the robustness and stability of the algorithm (adding noise
before and after the measurements). Our encode and update times are optimal up
to factors
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