Original scientific paper The architecture of the proposed Dual Layer Hybrid State Estimator starts with the linear state estimator that only takes synchrophasors as inputs and calculates states of the buses with the deployed Phasor Measurement Units (PMUs) and their adjacent buses. These states are used to obtain the pseudomeasurements that are processed together with conventional SCADA measur...

In this paper, further result on design problem of state estimator for a class of neural networks of neutral type is presented. A delay-dependent linear matrix inequality (LMI) criterion for existence of the estimator is derived. A numerical simulation is given to show the effectiveness of proposed estimator. 2008 Elsevier Inc. All rights reserved.

We derive a reliable a posteriori error estimator for a state-constrained elliptic optimal control problem taking into account both regularisation and discretisation. The estimator is applicable to finite element discretisations of the problem with both discretised and non-discretised control. The performance of our estimator is illustrated by several numerical examples for which we also introd...

A novel method is presented for identifying a state-space model and a state estimator for linear stochastic systems from input and output data. The method is primarily based on the relationship between the state-space model and the finite difference model of linear stochastic systems derived through projection filters. It is proved that least-squares identification of a finite difference model ...

We present a novel strategy to perform estimation for a dynamical mechanical system in standard operating conditions, namely, without ad hoc experimental testing. We adopt a sequential approach, and the joint state-parameter estimation procedure is based on a state estimator inspired from collocated feedback control. This type of state estimator is chosen due to its particular effectiveness and...

State estimators are vitally important in energy control centers. The measurements that come from control system are generally analysed by a state estimator. Since there can al\V<1ysbe bad measurements in the system, estimated value and the true value of the state estimator can be far from each other. In this paper, by using an artificial neural network (ANN), a bad data detection, identificati...

In this paper, we propose a new ridge-type estimator called the new mixed ridge estimator (NMRE) by unifying the sample and prior information in linear measurement error model with additional stochastic linear restrictions. The new estimator is a generalization of the mixed estimator (ME) and ridge estimator (RE). The performances of this new estimator and mixed ridge estimator (MRE) against th...

The ARMarkov models were originally developed for adaptive neural control, and later for predictive control, and state-space identification. Recently, an interaction matrix formulation has been developed that explains the internal structure of the ARMarkov models and their connection to the state-space representation. Using the interaction matrix formulation, we show in this paper how a state e...

I n the control of batch distillation columns, one of the problems is the difficulty in monitoring the compositions. This problem can be handled by estimating the compositions from readily available online temperature measurements using a state estimator. In this study, a state estimator that infers the product composition in a multicomponent batch distillation column (MBDC) from the temperatur...

Fault detection and electrical system monitoring (management) for aircraft/spacecraft dc or 400 Hz electrical systems is presented. Real-time ‘snapshot’ data is collected from current and voltage measurement transducers on radial or loop aircraft electrical system and introduced into a State Estimator. The State Estimator ‘smoothes’ the data, detects bad transducers, and calculates the best est...