9,389 research outputs found
State estimation of a solar direct steam generation mono-tube cavity receiver using a modified Extended Kalman Filtering scheme
State estimation plays a key role in the development of advanced control strategies for Concentrating Solar Thermal Power (CSP)
systems, by providing an estimate of process variables that are otherwise infeasible to measure. The present study proposes a state estimation
scheme for a once-through direct steam generation plant, the SG4 steam generation system at the Australian National University.
The state estimation scheme is a modified Extended Kalman Filter that computes an estimate of the internal variables of the mono-tube
cavity receiver in the SG4 system, from a dynamic non-linear model of the receiver. The proposed scheme augments the capabilities of a
Continuous-Direct Extended Kalman Filter to deal with the switched nature of the receiver, in order to produce estimates during system
start-up, cloud transients and operation of the plant. The estimation process runs at regular sample intervals and happens in two stages, a
prediction and a correction stage. The prediction stage uses the receiver model to calculate the evolution of the system and the correction
stage modifies the predicted estimate from measurements of the SG4 system. The resulting estimate is a set of internal variables describing
the current state of the receiver, termed the state vector. This paper presents a description of the modified Extended Kalman Filter
and an evaluation of the scheme using computer simulations and experimental runs in the SG4 system. Simulations and experimental
results in this paper show that the filtering scheme improves a receiver state vector estimation purely based on the receiver model
and provides estimates of a quality sufficient for closed loop control.This work has been supported by the Australian
Renewable Energy Agency (ARENA)
An exact minimum variance filter for a class of discrete time systems with random parameter perturbations
An exact, closed-form minimum variance filter is designed for a class of discrete time uncertain systems which allows for both multiplicative and additive noise sources. The multiplicative noise model includes a popular class of models (Cox-Ingersoll-Ross type models) in econometrics. The parameters of the system under consideration which describe the state transition are assumed to be subject to stochastic uncertainties. The problem addressed is the design of a filter that minimizes the trace of the estimation error variance. Sensitivity of the new filter to the size of parameter uncertainty, in terms of the variance of parameter perturbations, is also considered. We refer to the new filter as the 'perturbed Kalman filter' (PKF) since it reduces to the traditional (or unperturbed) Kalman filter as the size of stochastic perturbation approaches zero. We also consider a related approximate filtering heuristic for univariate time series and we refer to filter based on this heuristic as approximate perturbed Kalman filter (APKF). We test the performance of our new filters on three simulated numerical examples and compare the results with unperturbed Kalman filter that ignores the uncertainty in the transition equation. Through numerical examples, PKF and APKF are shown to outperform the traditional (or unperturbed) Kalman filter in terms of the size of the estimation error when stochastic uncertainties are present, even when the size of stochastic uncertainty is inaccurately identified
Multimode laser cooling and ultra-high sensitivity force sensing with nanowires
Photo-induced forces can be used to manipulate and cool the mechanical motion
of oscillators. When the oscillator is used as a force sensor, such as in
atomic force microscopy, active feedback is an enticing route to enhancing
measurement performance. Here, we show broadband multimode cooling of dB
down to a temperature of ~K in the stationary regime. Through the use
of periodic quiescence feedback cooling, we show improved signal-to-noise
ratios for the measurement of transient signals. We compare the performance of
real feedback to numerical post-processing of data and show that both methods
produce similar improvements to the signal-to-noise ratio of force
measurements. We achieved a room temperature force measurement sensitivity of
N with integration time of less than ms. The high
precision and fast force microscopy results presented will potentially benefit
applications in biosensing, molecular metrology, subsurface imaging and
accelerometry.Comment: 16 pages and 3 figures for the main text, 14 pages and 5 figures for
the supplementary informatio
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
Recommended from our members
Mixed H2/Hâ filtering for uncertain systems with regional pole assignment
Copyright [2005] IEEE. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Brunel University's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to [email protected]. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.The mixed H2/Hâ filtering problem for uncertain linear continuous-time systems with regional pole assignment is considered. The purpose of the problem is to design an uncertainty-independent filter such that, for all admissible parameter uncertainties, the following filtering requirements are simultaneously satisfied: 1) the filtering process is asymptotically stable; 2) the poles of the filtering matrix are located inside a prescribed region that compasses the vertical strips, horizontal strips, disks, or conic sectors; 3) both the H2 norm and the Hâ norm on the respective transfer functions are not more than the specified upper bound constraints. We establish a general framework to solve the addressed multiobjective filtering problem completely. In particular, we derive necessary and sufficient conditions for the solvability of the problem in terms of a set of feasible linear matrix inequalities (LMIs). An illustrative example is given to illustrate the design procedures and performances of the proposed method
Novel battery model of an all-electric personal rapid transit vehicle to determine state-of-health through subspace parameter estimation and a Kalman Estimator
Abstract--The paper describes a real-time adaptive
battery model for use in an all-electric Personal Rapid
Transit vehicle. Whilst traditionally, circuit-based models
for lead-acid batteries centre on the well-known Randlesâ
model, here the Randlesâ model is mapped to an equivalent
circuit, demonstrating improved modelling capabilities and
more accurate estimates of circuit parameters when used in
Subspace parameter estimation techniques. Combined with
Kalman Estimator algorithms, these techniques are
demonstrated to correctly identify and converge on voltages
associated with the battery State-of-Charge, overcoming
problems such as SoC drift (incurred by coulomb-counting
methods due to over-charging or ambient temperature
fluctuations).
Online monitoring of the degradation of these estimated
parameters allows battery ageing (State-of-Health) to be
assessed and, in safety-critical systems, cell failure may be
predicted in time to avoid inconvenience to passenger
networks.
Due to the adaptive nature of the proposed methodology,
this system can be implemented over a wide range of
operating environments, applications and battery
topologies
- âŠ