9,389 research outputs found

    State estimation of a solar direct steam generation mono-tube cavity receiver using a modified Extended Kalman Filtering scheme

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    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

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    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

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    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 −23-23 dB down to a temperature of 8±18 \pm 1~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 <2×10−16< 2\times10^{-16} N with integration time of less than 0.10.1 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

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    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

    Novel battery model of an all-electric personal rapid transit vehicle to determine state-of-health through subspace parameter estimation and a Kalman Estimator

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    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 Randles’ model, here the Randles’ 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
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