State Estimation Process Research Articles

Detection Against Linear Deception Attacks on Multi-Sensor Remote State Estimation

In this paper, a security problem in cyberphysical systems (CPS) is studied. A remote state estimation process using multiple sensors is considered. The measurement innovation packets from each sensor, which may be modified by a malicious attacker, are sent to a remote fusion center through wireless communication channels. To avoid being detected by typical bad data detectors at the remote estimator's side, the attacker would maintain the statistical properties of the measurements. Based on the information extracted from the trusted sensors and the correlations between the trusted sensors and the suspicious sensors, we propose three sequential data verification and fusion procedures for different detection information scenarios. The corresponding impacts of possible attacking patterns on the estimation performance under different detectors are analyzed explicitly. Simulations are provided to illustrate the developed results.

Augmented Prediction of Turbulent Flows via Sequential Estimators

A sensitivity analysis of new methodological approaches for state estimation (Meldi and Poux J. Comput. Phys. 347, 207–234, 2017) is proposed in this manuscript. The performance of the estimator is tested via the analysis of a number of aspects that play a major role in the augmented prediction process, such as the density in time sampling of available observation, the placement of sensors and the interaction with boundary conditions. The work is developed for the turbulent spatially evolving mixing layer test case, using high precision DNS samples as observation and Smagorinsky LES as underlying model. A number of estimators combining LES with DNS data integrated via sensors are performed, varying the frequency of time sampling of observation fT = 1/ΔT, where ΔT is the period between successive assimilation phases. It is concluded that if ΔT ≤≈ 0.5tA, where tA is the characteristic average advection time, the prediction via estimator shows minimal differences i.e. the process of state estimation has reached convergence. This relation can be interpreted as a threshold for converged state estimation. However, the results show as well that a linear converge towards pure model performance is not obtained for every physical quantity with progressive decrease of fT, while eventually pure model results are obtained for fT → 0. In addition, the effect of upstream boundary conditions over the state estimation are investigated and strategies for optimized positions of sensors are derived.

Tracking Three-Phase Untransposed Transmission Line Parameters Using Synchronized Measurements

The purpose of the study that is reported in this paper is to develop a method to estimate the parameters of untransposed overhead transmission lines using synchronized measurements. Considering the noise in the measured bus voltages and line currents, a three-phase static state estimation is employed first to estimate the states. Then, the parameters are tracked dynamically by a Kalman filter. State estimation and parameter tracking process are iteratively carried out to reduce the uncertainties that exist in the estimation function. In addition, a static parameter estimation method is proposed to estimate the initial values of line parameters. Several tests using simulated as well as actual recorded measurements are conducted considering a wide spectrum of line parameter variations. Results of these simulations are provided to illustrate how the proposed approach can successfully track line parameters under a variety of operating conditions.

Metering design for power networks using observability indicators

A state estimation process is highly influenced by the level of the measurement redundancies. However, due to financial and operational constraints, redundancy is in practice limited, making the power systems metering design a challenging problem. Therefore, for the first time, a methodology to design a metering system for state estimation in power systems is proposed, in which the metering placement is modeled as a combinatorial constrained optimization problem, where the final goal is to maximize the system redundancy – expressed by observability indicators – subjected to investment constraints. A Genetic Algorithm and a Greedy Randomized Adaptive Search Procedure, combined with a constructive heuristic are investigated for the metering design problem. Tests with IEEE-14, IEEE-30, IEEE-118 bus systems and part of a real Brazilian system are carried out to evaluate the performance of the proposed methodology. The results also show that, for the IEEE-118 bus system, 30% of the total cost of the measurements and RTUs is required to ensure observability, whereas 32% and 60% are necessary to eliminate the network critical measurements and to obtain a system without critical sets, respectively.

About control of guaranteed estimation

The control problem by parameters in the course of the guaranteed state estimation of linear non-stationary systems is considered. It is supposed that unknown disturbances in the system and the observation channel are limited by norm in the space of square integrable functions and the initial state of the system is also unknown. The process of guaranteed state estimation includes the solution of a matrix Riccati equation that contains some parameters, which may be chosen at any instant of time by the first player (an observer) and the second player (an opponent of the observer). The purposes of players are diametrically opposite: the observer aims to minimize diameter of information set at the end of observation process, and the second player on the contrary aims to maximize it. This problem is interpreted as a differential game with two players for the Riccati equation. All the choosing parameters are limited to compact sets in appropriate spaces of matrices. The payoff of the game is interpreted through the Euclidean norm of the inverse Riccati matrix at the end of the process. A specific case of the problem with constant matrices is considered. Methods of minimax optimization, the theory of optimal control, and the theory of differential games are used. Examples are also given.

Average Consensus for Multi‐Agent System with Measurement Noise and Binary‐Valued Communication

AbstractThis paper considers the average consensus problems of a class of multi‐agent systems (MAS) with binary‐valued communication. Each agent can only obtain its neighbor's binary‐valued information under measurement noise because of limited bandwidth in communication channels. To seek consensus, we propose a two‐scale multi‐agent consensus algorithm with distributed strategy by combining state estimation and control design alternately. An exponential step size is chosen in the state estimation process and the estimation method can be proved to be asymptotically efficient. Additionally, by utilizing a distributed control law designed based on the estimates of the neighbors' states with a constant gain, we further prove that the proposed average consensus algorithm is convergent. Furthermore, the proposed average consensus algorithm is given and proved. Finally, some simulation results, which illustrate the effectiveness of the obtained results, are also given in the paper.

A Robust False Data Detection in Smart Grid Using Trees Decision Algorithm

The smart grid has the objective of improving the control and the supervision of the electric grid with the aim to ensure a reliable and effective functioning; This is possible with a system based on a precise state estimate that accurately reflects the physical aspects of power grids. However, the progress of the grid element, basically the smart meter has considerably increased the number of access point to the electrical grid. As a result, a large flow of false data is generated in the electrical system that compromises the state estimation process. Unlike the conventional detection test, we propose a method capable of handling complex false data. Our analyzes are validated by experiments on a physical bus feeding system performed on PSS / E, then we use Matlab to build false data based on the Jacobian. The collected data is analyzed with a learning technique basing on decision tree method.

Monitoring mAb cultivations with in-situ raman spectroscopy: The influence of spectral selectivity on calibration models and industrial use as reliable PAT tool.

Raman spectroscopy is a suitable monitoring technique for CHO cultivations. However, a thorough discussion of peaks, bands, and region assignments to key metabolites and culture attributes, and the interpretability of produced calibrations is scarce. That understanding is vital for the long-term predictive ability of monitoring models, and to facilitate lifecycle management that comply with regulatory guidelines. Several fed-batch lab-scale mAb mammalian cultivations were carried out, with in situ Raman spectroscopy used for process state estimation and attribute monitoring. The goal was to evaluate its use as a process analytical technology (PAT) tool to detect residual glucose and lactate levels, understand their dynamics and interconversion, and eventually estimate key performance culture and product quality attributes. Glucose and lactate models were optimized up to 0.31 gL-1 with 3 Latent Variables (LVs) and 0.19 gL-1 (2 LVs) accuracy, respectively. Glutamine and product titer models, were not specific and accurate enough, even though indirect calibrations were obtained with a RMSEP of 0.12 gL-1 (4 LVs) and 0.29 gL-1 (5 LVs), respectively. A critical discussion and details about the extensive work done in calibration development and optimization are provided. Namely, considering a risk-based selection of variability sources impacting sample spectra, executing designed experiments with spiked cultivations, and using advanced chemometric procedures for variable selection and model cross validation. A strategy is presented to evaluation Raman spectroscopy as a reliable PAT technology fit-for industrial use. © 2018 American Institute of Chemical Engineers Biotechnol. Prog., 34:659-670, 2018.

State Estimation for General Complex Dynamical Networks with Incompletely Measured Information.

Estimating uncertain state variables of a general complex dynamical network with randomly incomplete measurements of transmitted output variables is investigated in this paper. The incomplete measurements, occurring randomly through the transmission of output variables, always cause the failure of the state estimation process. Different from the existing methods, we propose a novel method to handle the incomplete measurements, which can perform well to balance the excessively deviated estimators under the influence of incomplete measurements. In particular, the proposed method has no special limitation on the node dynamics compared with many existing methods. By employing the Lyapunov stability theory along with the stochastic analysis method, sufficient criteria are deduced rigorously to ensure obtaining the proper estimator gains with known model parameters. Illustrative simulation for the complex dynamical network composed of chaotic nodes are given to show the validity and efficiency of the proposed method.

Offset-free nonlinear Model Predictive Control with state-space process models

AbstractOffset-free model predictive control (MPC) algorithms for nonlinear state-space process models, with modeling errors and under asymptotically constant external disturbances, is the subject of the paper. The main result of the paper is the presentation of a novel technique based on constant state disturbance prediction. It was introduced originally by the author for linear state-space models and is generalized to the nonlinear case in the paper. First the case with measured state is considered, in this case the technique allows to avoid disturbance estimation at all. For the cases with process outputs measured only and thus the necessity of state estimation, the technique allows the process state estimation only - as opposed to conventional approach of extended process-and-disturbance state estimation. This leads to simpler design with state observer/filter of lower order and, moreover, without the need of a decision of disturbance placement in the model (under certain restrictions), as in the conventional approach. A theoretical analysis of the proposed algorithm is provided, under applicability conditions which are weaker than in the conventional approach. The presented theory is illustrated by simulation results of nonlinear processes, showing competitiveness of the proposed algorithms.

Open circuit voltage and state of charge online estimation for lithium ion batteries

Open circuit voltage (OCV), as a nonlinear function of state of charge (SoC) of lithium ion battery, commonly obtained through offline OCV test at certain ambient temperatures and aging stages. The OCV-SoC relationship may be inaccurate in real application due to the difference in operation conditions. In this paper, the OCV is identified by H infinity filter (HIF) in real operation conditions. Due to the no need to derive the jacobian matrices with unscented Kalman filter (UKF), the identified discrete OCV points are propagated to state estimation process instead of the traditional OCV-SoC nonlinear function. Additionally, the polarization voltage across the polarization capacitor is also passed to state estimation in the form of discrete data points. The experimental results demonstrate that the HIF-UKF can obtain the OCV and SoC in real time.

Joint Channel Decoding and State Estimation in Cyber-Physical Systems

Combined with a quickest detection algorithm for error propagation prevention in joint channel decoding and state estimation, a decoding procedure based on Pearl’s belief propagation is proposed to exploit the redundancy of system states in time domain for cyber-physical systems. By applying the proposed scheme to a linear model of electric generator dynamic system, numerical simulations have demonstrated that a better performance of state estimation will be obtained by the joint channel decoding and state estimation, and the error propagation can be successfully eliminated by the error propagation detection algorithm during the state estimation process.

Bayesian hybrid state estimation for unequal-length batch processes with incomplete observations

This paper investigates state estimation problem for batch processes with unequal-length batches as well as incomplete observations. A Bayesian hybrid state estimation method is proposed based on two dimensional (2D) correlations of states. The states of equal-length segment of time are estimated according to both within-a-batch and batch-to-batch correlations, and the states of unequal-length segment are obtained according to the correlations within the batch. In this way, the batch process states can be achieved in both equal-length and unequal-length situations, of which the latter one is a more general case. In order to approximate state distribution of nonlinear system and to deal with the problem of incomplete observations, particle filter (PF) is employed. The proposed method shows its superiority with a nonlinear system and a gas-phase reaction process. Compared to a typical existing method, the proposed method provides better estimation accuracy in the situation of equal-length batches, also it shows less sensitivity to incomplete observations.

Optimal WAMS hybrid configuration for voltage stability monitoring application considering the existence of conventional measurement

The supervisory control and data acquisition (SCADA) system is used in power systems worldwide because of its maturity and capability. However, the SCADA system is vulnerable to fast system changes. Wide area monitoring system (WAMS) based on phasor measurement units (PMUs), which requires high investment, is being thought of as one of the solutions in recent power systems. This paper proposes a procedure to upgrade SCADA into WAMS by installing sufficient numbers of additional PMUs without removing conventional measurements from the existing SCADA system. The WAMS hybrid configuration is focused on the optimal PMU placement and state estimation strategy for hybrid PMU–SCADA measurements. The optimal placement of PMUs is subject to the improvement in estimation accuracy. For guaranteeing secure operation, line contingencies and some important buses are considered as subproblems of PMU placement. The state estimation procedure manages the initial state variable and modifies the weighting factor strategy that is suitable for the hybrid system especially during fast system changes. The results show that the proposed WAMS hybrid guarantees better estimation accuracy and faster state estimation process than SCADA in both non‐contingency and contingency conditions. In addition, the WAMS hybrid has the capability to perform preventive strategy for securing voltage stability, which was previously applied in the PMU‐based WAMS system. © 2017 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Heart rate from face videos under realistic conditions for advanced driver monitoring

Abstract The role of physiological signals has a large impact on driver monitoring systems, since it tells something about the human state. This work addresses the recursive probabilistic inference problem in time-varying linear dynamic systems to incorporate invariance into the task of heart rate estimation from face videos under realistic conditions. The invariance encapsulates motion as well as varying illumination conditions in order to accurately estimate vitality parameters from human faces using conventional camera technology. The solution is based on the canonical state space representation of an Itô process and a Wiener velocity model. Empirical results yield to excellent real-time and estimation performance of heart rates in presence of disturbing factors, like rigid head motion, talking, facial expressions and natural illumination conditions making the process of human state estimation from face videos applicable in a much broader sense, pushing the technology towards advanced driver monitoring systems.

Online Denoising Based on the Second-Order Adaptive Statistics Model

Online denoising is motivated by real-time applications in the industrial process, where the data must be utilizable soon after it is collected. Since the noise in practical process is usually colored, it is quite a challenge for denoising techniques. In this paper, a novel online denoising method was proposed to achieve the processing of the practical measurement data with colored noise, and the characteristics of the colored noise were considered in the dynamic model via an adaptive parameter. The proposed method consists of two parts within a closed loop: the first one is to estimate the system state based on the second-order adaptive statistics model and the other is to update the adaptive parameter in the model using the Yule–Walker algorithm. Specifically, the state estimation process was implemented via the Kalman filter in a recursive way, and the online purpose was therefore attained. Experimental data in a reinforced concrete structure test was used to verify the effectiveness of the proposed method. Results show the proposed method not only dealt with the signals with colored noise, but also achieved a tradeoff between efficiency and accuracy.

State estimator for electrical distribution systems based on an optimization model

State estimation (SE) has become an important aspect of real-time management of electrical distribution systems (EDS). However, it is a challenging problem, mainly due to the network model complexity and the lack of a reliable measurement infrastructure capable to cope with a variety of network configurations. This work presents an Extended Optimal Power Flow (E-OPF) for SE in EDS that considers different network configurations. The active and reactive power loads are handled as extended variables in the optimization model that are constrained by upper and lower bounds obtained from historical data. The objective function combines the state estimation error (SEE) from the known Weighted Least Squares (WLS) approach with additional indexes related to the state variables, which improve the SE process. The combination of different indexes and the handling of power loads as optimization variables without needing precise historical data are novelty for SE and consist of the main contributions for an EDS that, in general, has few available data. The computational efficiency when different topologies are considered and in situations where the WLS estimator might fail is also a suitable feature of the proposed approach. The known 16-bus and 33-bus test systems are used to assess the proposed methodology. An illustrative application of the proposed approach in a Phasor Measurement Unit (PMU) allocation problem is also presented.

Simultaneous estimation of states and parameters in Newell’s simplified kinematic wave model with Eulerian and Lagrangian traffic data

The traffic state estimation process estimates various traffic states from available data in a road network and provides valuable information for travelers and decision makers to improve both travel experience and system performance. In many existing methods, model parameters and initial states have to be given in order to estimate traffic states, which limits the accuracy of the results as well as their transferability to different locations and times. In this paper, we propose a new framework to simultaneously estimate model parameters and traffic states for a congested road segment based on Newell’s simplified kinematic wave model (Newell, 1993). Given both Eulerian traffic count data and Lagrangian vehicle reidentification data, we formulate a single optimization problem in terms of the initial number of vehicles and model parameters. Then we decouple the optimization problem such that the initial number of vehicles can be analytically solved with a closed-form formula, and the model parameters, including the jam density and the shock wave speed in congested traffic, can be computed with the Gauss-Newton method. Based on Newell’s model, we can calculate individual vehicles’ trajectories as well as the average densities, speeds, and flow-rates inside the road segment. We also theoretically show that the optimization problem can have multiple solutions under absolutely steady traffic conditions. We apply the proposed method to the NGSIM datasets, verifying the validity of the method and showing that this method yields better results in the estimation of average densities than existing methods.

Robust and reliable estimation via recursive nonlinear dynamic data reconciliation based on cubature Kalman filter

Since measurements of process variables are subject to measurements errors as well as process variability, data reconciliation is the procedure of optimally adjusting measured date so that the adjusted values obey the conservation laws and constraints. Thus, data reconciliation for dynamic systems is fundamental and important for control, fault detection, and system optimization. Attempts to successfully implement estimators are often hindered by serve process nonlinearities, complicated state constraints, and un-measurable perturbations. As a constrained minimization problem, the dynamic data reconciliation is dynamically carried out to product smoothed estimates with variances from the original data. Many algorithms are proposed to solve such state estimation such as the extended Kalman filter (EKF), the unscented Kalman filter, and the cubature Kalman filter (CKF). In this paper, we investigate the use of CKF algorithm in comparative with the EKF to solve the nonlinear dynamic data reconciliation problem. First we give a broad overview of the recursive nonlinear data dynamic reconciliation (RNDDR) scheme, then present an extension to the CKF algorithm, and finally address the issue of how to solve the constraints in the CKF approach. The CCRNDDR method is proposed by applying the RNDDR in the CKF algorithm to handle nonlinearity and algebraic constraints and bounds. As the sampling idea is incorporated into the RNDDR framework, more accurate estimates can obtained via the recursive nature of the estimation procedure. The performance of the CKF approach is compared with EKF and RNDDR on nonlinear process systems with constraints. The conclusion is that with an error optimization solution of the correction step, the reformulated CKF shows high performance on the selection of nonlinear constrained process systems. Simulation results show the CCRNDDR is an efficient, accurate and stable method for real-time state estimation for nonlinear dynamic processes.

Between the Poles of Data‐Driven and Mechanistic Modeling for Process Operation

AbstractThe best method for process control is the use of model‐based solutions, based on process analytical technology for online monitoring of critical process variables, product quality attributes, or a holistic process state estimation. Mechanistic models as well as data‐driven techniques are essential for real‐time process monitoring. Their main characteristics, advantages and disadvantages, and the link between both are discussed as well as the synergetic effects, benefits, and drawbacks resulting from their combination. Aspects and differences of the computational model life cycle management are highlighted.