Inferential measurement of SAG mill parameters II: state estimation

A finite difference method (FDM) solving the coupled partial differential equations governing three-dimensional (3D) motions of a towed underwater cable has been implemented in a combined ensemble Kalman filter (EnKF) and ensemble Kalman smoother (EnKS), as a new approach to combined state and parameter estimation for towed underwater cables. A simulation study of the method applied to a seismic streamer has been performed. Cable state variables as well as model parameters are estimated. Parameters estimated are crossline ocean current varying with time as well as cable tangential drag coefficient. The presented results indicate that the method is able to estimate state as well as parameters for seismic streamers.

In this paper, the set-membership state and parameter estimation problems are considered for linear time-varying systems. The noises in the system are unknown but bounded and the variation of parameters is considered as a bounded expansion. The entire estimator is an interactive estimation of parameters and states. In parameter estimation, the exact set of parameter estimates may be non-convex because of the uncertain states. A polytopic linear parameter varying (LPV) enclosure of the system regression expression is constructed as a convex relaxation. The result of parameter estimation is a set to include the true parameter of the model. Meanwhile, in the state estimation, a similar LPV enclosure of the state-space model is also used to bound the state matrix uncertainties. This LPV enclosure in the state estimation is narrowed by the parameter estimation, and then the set of state estimates is computed to include the true state. The constrained zonotope (CZ) is used to describe sets of interest and the necessary set operations are expanded to LPV mapping. Because of the highly adjustable accuracy of CZ, the proposed algorithm can make a better trade-off between computational accuracy and efficiency. Finally, a numerical example is given to illustrate the effectiveness of the proposed algorithm.

Combined heat and power networks effectively improve energy efficiency. It is necessary to get a global solution with high accuracy for the combined networks, which is the basis of the subsequent analysis and optimization. State estimation has been widely adopted in the power system to decrease errors caused by measurements. Similarly, state estimation can also be used in the district heating network. State estimation for the district heating network is developed in this paper with a simplified dynamic model by the weighted least-square method. However, the state estimation for each network individually doesn't give a global solution. So, it is necessary to take coupling components into consideration. The proposed state estimation is for the entire combined power and heat networks. Because of the increased coupling constraints equations, the redundancy of the state estimation increases, which helps to improve the accuracy. This paper shows the comparison between the combined state estimation and the individual state estimation. The combined state estimation gives a global solution with higher precision, which helps the subsequent analysis and optimization.

State estimation (SE) is an important task allowing power networks to monitor accurately the underlying system state, which is useful for security-constrained dispatch and power system control. For nonlinear AC power systems, SE amounts to minimizing a weighted least-squares cost that is inherently nonconvex, thus giving rise to many local optima. As a result, estimators used extensively in practice rely on iterative optimization methods, which are destined to return only locally optimal solutions, or even fail to converge. A semidefinite programming (SDP) formulation for SE has been advocated, which relies on the convex semidefinite relaxation (SDR) of the original problem and thereby renders it efficiently solvable. Theoretical analysis under simplified conditions is provided to shed light on the near-optimal performance of the SDR-based SE solution at polynomial complexity. The new approach is further pursued toward complementing traditional nonlinear measurements with linear synchrophasor measurements and reducing computational complexity through distributed implementations. Numerical tests on the standard IEEE 30- and 118-bus systems corroborate that the SE algorithms outperform existing alternatives, and approach near-optimal performance.

We propose a state estimation approach, enhanced with prior information, for dynamic magnetic resonance imaging (MRI). In state estimation the time series of system states are estimated, utilizing the observation and state evolution models and the dynamic time-varying measurements. In this work the state estimation algorithm is the Kalman filter (KF), where the state estimation formulation is complemented with an anatomical structural prior. In our previous work we used two different methods to include prior information to KF. In the first method the observation matrix was augmented with anatomical prior information and in the second method a heuristic total variation (TV) denoising was applied to each Kalman recursion. While the TV method was faster and better than the augmented one, the TV method lacked a sound mathematical background. In this work, we include the prior information in the state evolution model allowing for significantly faster computation compared to the previous augmented method, yet retaining the solid mathematical background and the same quality of the estimate.The proposed approach is evaluated using simulated functional MRI (fMRI) data and experimental, golden angle sampled, small animal dynamic contrast-enhanced MRI data from a rat brain. When using a state estimation method, we can improve the temporal resolution compared to conventional methods by using only one spoke of radial data for each estimate. We compare the results to the augmented Kalman filter and a sliding window method. The results show that the proposed method produces equally good results when compared to the augmented Kalman filter, but allows for over five times shorter computational times.

We here investigated a non-linear ensemble Kalman filter (SPKF) application to a motor imagery brain computer interface (BCI). A square root central difference Kalman filter (SR-CDKF) was used as an approach for brain state estimation in motor imagery task performance, using scalp electroencephalography (EEG) signals. Healthy human subjects imagined left vs. right hand movements and tongue vs. bilateral toe movements while scalp EEG signals were recorded. Offline data analysis was conducted for training the model as well as for decoding the imagery movements. Preliminary results indicate the feasibility of this approach with a decoding accuracy of 78%-90% for the hand movements and 70%-90% for the tongue-toes movements. Ongoing research includes online BCI applications of this approach as well as combined state and parameter estimation using this algorithm with different system dynamic models.

Techniques for reconstructing or estimating unmeasured circuit variables and parameters in power electronic circuits are investigated. In numerous applications in power electronic systems, measurements of circuit variables are required for closed-loop control purposes or for diagnostic purposes, but are expensive or impossible to obtain directly. The method for state estimation relies on observer theory and is based on an intrinsic Lyapunov function, termed the energy in the increment. For cases where circuit parameters are not completely known, an adaptive estimation algorithm is developed for estimating the unknown circuit parameters along with the unmeasured circuit variables. The exponential stability of the combined parameter and state estimation scheme is established using averaging and singular perturbation analysis. Averaging analysis is used in off-line calculations to determine the feasibility of estimating certain parameters and to determine a basis from which to choose parameter update gains. Examples with an up-down converter illustrate the results. A hardware implementation of the state estimation scheme is also documented along with a description of a hardware implementation for parameter estimation.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

COVID-19 pandemic response with non-pharmaceutical interventions is an intrinsic control problem. Governments weigh social distancing policies to avoid overload in the health system without significant economic impact. The mutability of the SARS-CoV-2 virus, vaccination coverage, and mobility restriction measures change epidemic dynamics over time. A model-based control strategy requires reliable predictions to be efficient on a long-term basis. In this paper, a SEIR-based model is proposed considering dynamic feedback estimation. State and parameter estimations are performed on state estimators using augmented states. Three methods were implemented: constrained extended Kalman filter (CEKF), CEKF and smoother (CEKF & S), and moving horizon estimator (MHE). The parameters estimation was based on vaccine efficacy studies regarding transmissibility, severity of the disease, and lethality. Social distancing was assumed as a measured disturbance calculated using Google mobility data. Data from six federative units from Brazil were used to evaluate the proposed strategy. State and parameter estimations were performed from 1 October 2020 to 1 July 2021, during which Zeta and Gamma variants emerged. Simulation results showed that lethality increased between 11 and 30% for Zeta mutations and between 44 and 107% for Gamma mutations. In addition, transmissibility increased between 10 and 37% for the Zeta variant and between 43 and 119% for the Gamma variant. Furthermore, parameter estimation indicated temporal underreporting changes in hospitalized and deceased individuals. Overall, the estimation strategy showed to be suitable for dynamic feedback as simulation results presented an efficient detection and dynamic characterization of circulating variants.

The representer method for state and parameter estimation in single-phase Darcy flow

Particle methods, also known as Sequential Monte Carlo, have been ubiquitous for Bayesian inference for state-space models, particulary when dealing with nonlinear non-Gaussian scenarios. However, in many practical situations, the state-space model contains unknown model parameters that need to be estimated simultaneously with the state. In this paper, We discuss a sequential analysis for combined parameter and state estimation. An online learning method is proposed to approach the distribution of the model parameter by tuning a flexible proposal mixture distribution to minimize their Kullback-Leibler divergence. We derive the sequential learning method by using a truncated Dirichlet processes normal mixture and present a general algorithm under a framework of the auxiliary particle filtering. The proposed algorithm is verified in a blind deconvolution problem, which is a typical state-space model with unknown model parameters. Furthermore, in a more challenging application that we call meta-modulation, which is a more complex blind deconvolution problem with sophisticated system evolution equations, the proposed method performs satisfactorily and achieves an exciting result for high efficiency communication.

Observability analysis for state estimation using Hachtel’s augmented matrix method

This paper is concerned about the state estimation of power systems, which contain flexible ac transmission system devices. Specifically, the formulation of state estimation in order to incorporate the detailed steady state models of the unified power-flow controller (UPFC) is investigated. This formulation involves the use of equality and inequality constraints that account for the limits associated with the device operation and ratings. Furthermore, in addition to its use as an estimator, the developed estimator can also be used to determine the required settings for the control parameters of UPFCs for a given operating condition. Computational issues concerning initialization, step length, and matrix conditioning are also discussed and practical solutions are offered. Simulations on IEEE test systems of sizes ranging from 14 to 118 buses are presented to demonstrate the use of the developed program both as a state estimator and as a tool for setting the parameters of unified power-flow controllers.

This manuscript deals with parametric and state estimation problem of discrete-time nonlinear polynomials systems. In this respect, the aim is to design a Recursive State and Parametric Estimation (RSPE) algorithm, based on least square principle, polynomial adjustable models and Kalman filter theory. Our simulation results show the validity of the proposed algorithm.

Guest Editorial: Situational awareness of integrated energy systems

We present a distributed observer for nonlinear systems, which can be used to estimate the state of a central plant with unknown parameters using a network of sensors that measure noisy linear combinations of the state. Our state estimator is based on the unscented Kalman filter and the dual Kalman filter method, which runs two Kalman filters in parallel for both state and parameter estimation. This allows us to improve estimates of the central plants parameters in order to improve state estimates, and vice versa. Furthermore, using results from stochastic stability analysis, we prove that under certain conditions the expected value of the proposed algorithm's estimation error is bounded. This result also shows us that as t goes to infinity, the error bound scales as a function of the largest eigenvalue of the sensor noise covariance matrix. Lastly, we demonstrate the ability of our state estimator to accurately track the state of nonlinear systems through simulations.