Sliding mode functional observers for descriptor systems

This paper addresses the problem of estimating a linear function of the states of a descriptor system using sliding mode functional observer approach. Sliding mode functional observers proposed in this paper are of low-order and do not include the derivatives of the outputs. New conditions for the existence of sliding mode functional observers are derived. A design procedure for the determination of the observer parameters can also be easily derived based on the derived existence conditions

Time-delay systems : design of delay-free and low-order observers

This note provides a comprehensive treatment on the design of functional observers for linear systems having a time-varying delay inthe state variables. The designed observers possess attractive features of being low-order and delay-free and hence they are cost effective and easy to implement. Existence conditions are derived and a design procedure for finding low-order observers is given

A functional observer based dynamic state estimation technique for grid connected solid oxide fuel cells

IEEE This paper presents a functional observer based technique for estimating gaseous partial pressures in Triple Phase Boundary of a high-order Solid Oxide Fuel Cell. Triple Phase Boundary is a nano-scale region in Solid Oxide Fuel Cells where direct measurement of partial pressure of individual gases is not possible. For a reliable and a safe operation those quantities must be monitored. This paper reports a novel functional observer based dynamic state estimation approach that utilizes a system decomposition algorithm to provide a functional observer with minimum order. Therefore, the proposed technique has a simpler structure than conventional state observer based schemes. Case studies of the proposed technique, implemented on a complex nonlinear power system, show accurate and smooth estimations in comparison to full-order state observer based techniques in terms of tracking of nonlinear partial pressures

Design of sliding mode functional observers for time-delay systems of neutral type

This paper addresses the problem of estimating a linear function of the states of a class of linear time-delay systems of the neutral-type using sliding mode functional observer approach. Sliding mode functional observers proposed in this paper are of low-order and do not include the derivatives of the outputs. New conditions for the existence of sliding mode functional observers are derived. A design procedure for the determination of the observer parameters can also be easily derived based on the derived existence conditions

Sliding mode functional observers for nonlinear systems

This paper addresses the problem of estimating a linear function of the states of a nonlinear system using sliding mode functional observer approach. Sliding mode functional observers proposed in this paper are of low-order. New conditions for the existence of sliding mode functional observers are derived. A design procedure for the determination of the observer parameters can also be easily derived based on the derived existence conditions

Sliding mode functional observers with unknown inputs

This paper illustrates a method of designing a sliding mode linear functional observer for a system with unknown inputs. The necessary and sufficient conditions that will ensure the observers existence are defined. A structure and design algorithm for the sliding mode observer is proposed and it will be shown that this will apply to any detectable and controllable linear time invariant system

Sliding Mode Functional Observers for Nonlinear Systems

This paper addresses the problem of estimating a linear function of the states of a nonlinear system using sliding mode functional observer approach. Sliding mode functional observers proposed in this paper are of low-order. New conditions for the existence of sliding mode functional observers are derived. A design procedure for the determination of the observer parameters can also be easily derived based on the derived existence conditions

A novel neural network approach to dynamic state estimation of generators subjected to ageing in complex power systems

In this paper, a neural network based technique for estimating dynamic states of generators in highly complex power systems is presented. The proposed method is independent to the mathematical model of the generators and uses a nonlinear autoregressive neural network with exogenous inputs to estimate dynamic states of the generators. The proposed technique has been compared to particle filter and unscented Kalman filter based schemes previously reported in the literature. The simulation results show superiority of the proposed technique over the two other schemes when parameters of the generators alter. Parameter alterations in generators are practically occur due to environment impacts, ageing of the equipment and so on. The proposed scheme is capable of keeping its accuracy and precision even in the presence of unobservable variances in generator parameters. © 2019 IEEE

Power system dynamic state estimation using particle filter

A particle filter based power system dynamic state estimation scheme is presented in this paper. The proposed method can be considered as an alternative to the other schemes which are mostly based on the Kaiman Filter. The particle filter approach can be used to estimate the states of nonlinear systems which are subjected to both Gaussian and non-Gaussian noise. Furthermore, the presented scheme has a simple algorithm that can be easily implemented numerically. The case study considered in this paper reveals that the method has considerable accuracy and provides smooth dynamic state estimation even when the noise variance differs from a known initial value. © 2014 IEEE

Power system dynamic state estimation using particle filter

A particle filter based power system dynamic state estimation scheme is presented in this paper. The proposed method can be considered as an alternative to the other schemes which are mostly based on the Kaiman Filter. The particle filter approach can be used to estimate the states of nonlinear systems which are subjected to both Gaussian and non-Gaussian noise. Furthermore, the presented scheme has a simple algorithm that can be easily implemented numerically. The case study considered in this paper reveals that the method has considerable accuracy and provides smooth dynamic state estimation even when the noise variance differs from a known initial value. © 2014 IEEE