605 research outputs found
Combinatorial Penalties: Which structures are preserved by convex relaxations?
We consider the homogeneous and the non-homogeneous convex relaxations for
combinatorial penalty functions defined on support sets. Our study identifies
key differences in the tightness of the resulting relaxations through the
notion of the lower combinatorial envelope of a set-function along with new
necessary conditions for support identification. We then propose a general
adaptive estimator for convex monotone regularizers, and derive new sufficient
conditions for support recovery in the asymptotic setting
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Uncertainty modelling in power system state estimation
This thesis was submitted for the degree of Doctor of Philosophy and was awarded by Brunel University.As a special case of the static state estimation problem, the load-flow problem is studied
in this thesis. It is demonstrated that the non-linear load-flow formulation may be solved
by real-coded genetic algorithms. Due to its global optimisation ability, the proposed
method can be useful for off-line studies where multiple solutions are suspected.
This thesis presents two methods for estimating the uncertainty interval in power system
state estimation due to uncertainty in the measurements. The proposed formulations are
based on a parametric approach which takes in account the meter inaccuracies. A nonlinear
and a linear formulation are proposed to estimate the tightest possible upper and
lower bounds on the states. The uncertainty analysis, in power system state estimation, is
also extended to other physical quantities such as the network parameters. The
uncertainty is then assumed to be present in both measurements and network parameters.
To find the tightest possible upper and lower bounds of any state variable, the problem is
solved by a Sequential Quadratic Programming (SQP) technique.
A new robust estimator based on the concept of uncertainty in the measurements is
developed here. This estimator is known as Maximum Constraints Satisfaction (MCS).
Robustness and performance of the proposed estimator is analysed via simulation of
simple regression examples, D.C. and A.C. power system models.Embassy of Kuwai
On the application of a hybrid ellipsoidal-rectangular interval arithmetic algorithm to interval Kalman filtering for state estimation of uncertain systems
Modelling uncertainty is a key limitation to the applicability of the classical Kalman filter for state estimation of dynamic systems. For such systems with bounded modelling uncertainty, the interval Kalman filter (IKF) is a direct extension of the former to interval systems. However, its usage is not yet widespread owing to the over-conservatism of interval arithmetic bounds. In this paper, the IKF equations are adapted to use an ellipsoidal arithmetic that, in some cases, provides tighter bounds than direct, rectangular interval arithmetic. In order for the IKF to be useful, it must be able to provide reasonable enclosures under all circumstances. To this end, a hybrid ellipsoidal-rectangular enclosure algorithm is proposed, and its robustness is evidenced by its application to two characteristically different systems for which it provides stable estimate bounds, whereas the rectangular and ellipsoidal approaches fail to accomplish this in either one or the other case
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