471 research outputs found
Comparing Kalman Filters and Observers for Power System Dynamic State Estimation with Model Uncertainty and Malicious Cyber Attacks
Kalman filters and observers are two main classes of dynamic state estimation
(DSE) routines. Power system DSE has been implemented by various Kalman
filters, such as the extended Kalman filter (EKF) and the unscented Kalman
filter (UKF). In this paper, we discuss two challenges for an effective power
system DSE: (a) model uncertainty and (b) potential cyber attacks. To address
this, the cubature Kalman filter (CKF) and a nonlinear observer are introduced
and implemented. Various Kalman filters and the observer are then tested on the
16-machine, 68-bus system given realistic scenarios under model uncertainty and
different types of cyber attacks against synchrophasor measurements. It is
shown that CKF and the observer are more robust to model uncertainty and cyber
attacks than their counterparts. Based on the tests, a thorough qualitative
comparison is also performed for Kalman filter routines and observers.Comment: arXiv admin note: text overlap with arXiv:1508.0725
Data Assimilation by Conditioning on Future Observations
Conventional recursive filtering approaches, designed for quantifying the
state of an evolving uncertain dynamical system with intermittent observations,
use a sequence of (i) an uncertainty propagation step followed by (ii) a step
where the associated data is assimilated using Bayes' rule. In this paper we
switch the order of the steps to: (i) one step ahead data assimilation followed
by (ii) uncertainty propagation. This route leads to a class of filtering
algorithms named \emph{smoothing filters}. For a system driven by random noise,
our proposed methods require the probability distribution of the driving noise
after the assimilation to be biased by a nonzero mean. The system noise,
conditioned on future observations, in turn pushes forward the filtering
solution in time closer to the true state and indeed helps to find a more
accurate approximate solution for the state estimation problem
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