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This paper presents two novel estimation architectures to combine SCADA and PMU measurements into multistage strategies for Power System State Estimation. The first architecture makes use of a priori state information concepts and a blocked version of orthogonal Givens rotations with the objective of enhancing SCADA based estimates through PMU data processing. The second strategy is a three-stage...
This paper deals with computational aspects of interval kalman filtering of discrete time linear models with bounded uncertainties on parameters and gaussian measurement noise. In this work, we consider an extension of conventional Kalman filtering to interval linear models [1]. As the expressions for deriving the Kalman filter involve matrix inversion which is known to be a difficult problem. One...
In this paper, the problem of state estimation is considered for discrete-time stochastic linear systems subject to both partially observed inputs and multiple missing sensor measurements. First, the partially available information on the unknown inputs and the state equation are used to form the prior distribution of the state vector at each step. To obtain an analytically tractable likelihood function,...
State estimation for nonlinear systems generally requires approximations of the system or the probability densities, as the occurring prediction and filtering equations cannot be solved in closed form. For instance, linear regression Kalman filters like the unscented Kalman filter or the considered Gaussian filter propagate a small set of sample points through the system to approximate the posterior...
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