Reliable State Estimation Research Articles

Bayesian Framework for Multi-Timescale State Estimation in Low-Observable Distribution Systems

To support the smart grid paradigm, there has been a significant increase in sensor deployments and metering infrastructure in distribution systems. However, the measurements provided by these sensors and metering devices are typically sampled at different rates and could suffer from losses during the aggregation process. It is crucial to effectively reconcile the time-series measurements for a reliable state estimation. While weighted least squares has been the traditional approach for state estimation, sparsity-based approaches like matrix completion have become popular due to their superior performance in low-observability conditions. This paper proposes a Bayesian framework for both multi-timescale data aggregation and matrix completion based state estimation. Specifically, the multi-scale time-series data aggregated from heterogenous sources are reconciled using a multitask Gaussian process that exploits the spatio-temporal correlations. The resulting consistent time-series alongwith the confidence bound on the imputations are fed into a Bayesian matrix completion method augmented with linearized power-flow constraints to accurately estimate the states in low-observability conditions. Results on three phase unbalanced IEEE 37 and IEEE 123 bus test systems reveal the superior performance of the proposed Bayesian framework. The computational complexity for the proposed Bayesian framework is also quantified.

A Data-Driven Snapshot Method for State of Health Modelling and Diagnostics of Maritime Battery Systems

Battery systems are increasingly being used for powering ocean going ships, and the number of fully electric or hybrid ships relying on battery power for propulsion and manoeuvring is growing. In order to ensure the safety of such electric ships, it is of paramount importance to monitor the available energy that can be stored in the batteries, and classification societies typically require that the state of health of the batteries can be verified by independent tests - annual capacity tests. However, this paper discusses data-driven diagnostics for state of health modelling for maritime battery systems based on operational sensor data collected from the batteries as an alternative approach. There are different strategies for such data-driven diagnostics. Some approaches, referred to as cumulative damage models, require full operational history of the batteries in order to predict state of health, and this may be impractical due to several reason. Thus, snapshot methods that are able to give reliable estimation of state of health based on only snapshots of the data streams are attractive candidates for data-driven diagnostics of battery systems on board ships. In this paper, data-driven snapshot methods are explored and applied to a novel set of degradation data from battery cells cycled in laboratory tests. The paper presents the laboratory tests, the resulting battery data, shows how relevant features can be extracted from snapshots of the data and presents data-driven models for state of health prediction. It is discussed how such methods could be utilized in a data-driven classification regime for maritime battery systems. Results are encouraging, and yields reasonable degradation estimates for 40% of the tested cells. This is greatly improved if data from the actual cell is included in the training data, and indicates that better results can be achieved if more representative training data is available. Nevertheless, improved accuracy is required for such snapshot methods to be recommended for ships in actual operation.

State of health estimation for lithium-ion batteries based on incremental capacity analysis under slight overcharge voltage

Accurate and reliable estimation of state of health (SOH) for lithium-ion batteries under slight overcharge voltage cycling has great significance for battery management systems. In this study, commercial lithium-ion phosphate batteries are investigated under slight overcharge voltage cycling. The aging mechanism is discussed based on incremental capacity analysis and differential voltage analysis. Moreover, the syncretic health indicator is obtained from the incremental capacity curves based on principal component analysis. Specifically, the capacity retention and Coulombic efficiency are analyzed under slight overcharge voltage cycling. The incremental capacity peaks (i.e., peak B and peak C) are discussed to extract potential health indicators, and a syncretic health indicator is adopted based on principal component analysis. Finally, the Gaussian process regression is established for accuracy SOH estimation with a 95% confidence interval under small data of slight overcharge cycling. In comparison with the traditional methods, the proposed method exhibits higher accuracy with a 95% confidence interval, and the error is limited to 3%.

Artificial Neural Networks – Based Method for Enhancing State Estimation of Grids with High Penetration of Renewables

This paper addresses state estimation as one of the most essential mechanisms in real-time operation and control of modern power systems, and proposes a novel solution to the issue of poor network observability, commonly faced in distribution system state estimation (DSSE) characterized by an ever-increasing penetration of renewable generation. The ongoing transformation from conventional passive, onedirectional power systems to active smart grids necessitates more accurate and reliable system state estimation to achieve optimal system performance. Real-time grid monitoring and control has been a routine task in transmission networks, but distribution grids cannot successfully utilize these capabilities due to different topologies, specific electrical characteristics, the low amount of available real-time measurements, as well as substantial communication effort needed to handle the data. Furthermore, with the advent of distributed generation, new types of loads and the vast surge of prosumers, a substantial amount of data is required to maintain system stability and controllability. For these reasons, reliable state estimation requires a high-quality creation process of pseudo-measurement, in addition to an efficient algorithm and an extremely accurate estimator. Thus, this paper proposes a novel framework of dynamic estimation methodology that includes the use of Artificial Neural Networks (ANN) in the pseudo-measurements generation process, utilizes Iteratively Reweighted Least Squares (IRWLS) algorithm and Schweppe-Huber Generalized Maximum Likelihood (SHGM) estimator. The efficiency and accuracy of the proposed methodology were assessed and verified on a benchmark network model.

Security-constrained optimal placement of PMUs using Crow Search Algorithm

For precise power system monitoring, a major focus is on involvement of the latest technology based on phasor measurement units (PMUs). As the sole system monitor, state estimator plays an important role in the security of power system operations. Optimal placement of PMUs (OPP) with numerical observability ensures reliable state estimation. For economical and efficient utilization, there is a need to optimize the placement of PMUs in the power system network. A new approach called Crow Search Algorithm (CSA) devised by others, has been used to solve an OPP problem. The performance of this new approach is compared to the dominant method for an optimization problem — binary integer linear programming (BILP). Comparison studies have also been carried out with particle swarm optimization (PSO) method. The major constraints such as topological, numerical observability conditions with and without zero-injection buses (ZIBs) are considered. Contingencies and limitation of measurement channels in a PMU device are also incorporated as constraints. The main advantage of using the CSA is that it provides multiple location sets for same optimal number of PMUs (optimal number same as obtained by BILP). While BILP method provides only one set of locations for the optimal number of PMUs obtained. This becomes advantageous in planning stage for power engineers for placing PMUs. Test systems considered for the case studies are of varied sizes such as IEEE 14-bus, IEEE 30-bus, IEEE 57-bus and 72-bus practical equivalent system of Indian southern region power grid.

Uncertainty quantification in LV state estimation under high shares of flexible resources

The ongoing electrification introduces new challenges to distribution system operators (DSOs). Controllable resources may simultaneously react to price signals, potentially leading to network violations. DSOs require reliable and accurate low-voltage state estimation (LVSE) to improve awareness and mitigate such events. However, the influence of flexibility activations on LVSE has not been addressed yet. It remains unclear if flexibility-induced uncertainty can be reliably quantified to enable robust DSO decision-making. In this work, uncertainty quantification in LVSE is systematically investigated for multiple scenarios of input information availability and flexibility usage, using real data. For that purpose, a Bayesian neural network (BNN) is compared to quantile regression. Results show that frequent flexibility activations can significantly deteriorate LVSE performance, unless secondary substation measurements are available. Moreover, it is demonstrated that the BNN captures flexibility-induced voltage drops by dynamically extending the prediction interval during activation periods, and that it improves interpretability regarding the cause of uncertainty.

A multi-fidelity ensemble Kalman filter with hyperreduced reduced-order models

We present an efficient data assimilation framework for nonlinear dynamical systems that uses multi-fidelity statistical estimates based on a full-order model and a projection-based hyperreduced order model (ROM) that is trained on-the-fly. This framework is particularly applicable to conservation laws in aerodynamics that yield expensive forward models. The formulation comprises the following technical components: (i) an ensemble Kalman filter to tractably handle high-dimensional, strongly nonlinear dynamical models; (ii) multi-fidelity forecast models, where ROM-based coarse fidelities are constructed on-the-fly; and (iii) hyperreduction for the ROM, based on proper orthogonal decomposition and the empirical quadrature procedure, constructed using the ensemble of full order model trajectories. We show that the multi-fidelity statistical estimates based on efficient, on-the-fly construction of the ROM enables rapid and reliable state estimation for practical nonlinear dynamical systems. We demonstrate the effectiveness of our framework to estimate the state of a separated flow around an airfoil by (i) showing that the ROM-based multi-fidelity method is more accurate than the single-fidelity method for comparable computational cost and (ii) showing that multi-fidelity statistics enable the use of a less accurate surrogate when compared against ROM-only filters, at negligible cost.

The emerging infrastructure of US firearms injury data

For every fatal shooting in the United States, detailed information from reports of coroners or medical examiners, police departments, and other sources is recorded in the National Violent Death Reporting System. There is no such system in place for nonfatal shootings, which far outnumber fatalities. Hospital data systems are in place that could, with some improvements, provide access to reliable local, state and national estimates of firearm injuries. Such estimates are possible because most firearms injuries are treated in hospitals, and hospitals routinely assign “external cause of injury” codes to all injury encounters. Federal health agencies supervise a number of data systems that centralize hospital data. Challenges currently being addressed are public access, timeliness, and accuracy of coding of intent. (Hospitals misclassify many firearm assaults as accidents.)Law enforcement agencies provide detailed data on shootings in criminal circumstances, including shootings that are not treated in a hospital. The FBI's Uniform Crime Reports (UCR) system aggregates data from agencies. The FBI instituted a radical reform of this system beginning in 2021, resulting in a sharp agency participation drop that prevents valid national estimates. The reform requires agencies to report incident-level data instead of summary counts, which is all that was required for the previous 90 years. There are ongoing efforts to increase participation in the new system and restore its former status as the leading source of national crime estimates. In the meantime, data on nonfatal gunshot cases are available from a number of police departments.We discuss additional reforms needed to generate timely, accurate, publicly accessible data from hospitals and police.

A Gaussian mixture filter with adaptive refinement for nonlinear state estimation

The state estimation of highly nonlinear dynamic systems is difficult because the probability distribution of their states can be highly non-Gaussian. An adaptive Gaussian mixture filter is developed in this work to address this challenge, in which the Gaussian mixture models are refined based on the system's local severity of nonlinearity to attain a high-fidelity estimation of the state distribution. A set of nonlinearity assessment criteria are designed to trigger the splitting of Gaussian components at both the prediction and update stages of Bayesian filtering and the error bound of estimated distribution is established. The new filter has been benchmarked against the existing methods on two challenging problems and it consistently provides among-the-best accuracy with a reasonable computational cost, which proves that it can be used as a reliable state estimator for engineering systems with highly nonlinear dynamics and subject to high magnitudes of uncertainties.

A Regression-Based Technique for Capacity Estimation of Lithium-Ion Batteries

Electric vehicles (EVs) and hybrid vehicles (HEVs) are being increasingly utilized for various reasons. The main reasons for their implementation are that they consume less or do not consume fossil fuel (no carbon dioxide pollution) and do not cause sound pollution. However, this technology has some challenges, including complex and troublesome accurate state of health estimation, which is affected by different factors. According to the increase in electric and hybrid vehicles’ application, it is crucial to have a more accurate and reliable estimation of state of charge (SOC) and state of health (SOH) in different environmental conditions. This allows improving battery management system operation for optimal utilization of a battery pack in various operating conditions. This article proposes an approach to estimate battery capacity based on two parameters. First, a practical and straightforward method is introduced to assess the battery’s internal resistance, which is directly related to the battery’s remaining useful life. Second, the different least square algorithm is explored. Finally, a promising, practical, simple, accurate, and reliable technique is proposed to estimate battery capacity appropriately. The root mean square percentage error and the mean absolute percentage error of the proposed methods were calculated and were less than 0.02%. It was concluded the geometry method has all the advantages of a recursive manner, including a fading memory, a close form of a solution, and being applicable in embedded systems.

Tracking of Maneuvering Extended Target Using Modified Variable Structure Multiple-Model Based on Adaptive Grid Best Model Augmentation

Maneuvering extended target tracking, an important but challenging research field, has attracted increasing attention in the field of radar signal processing. Variable structure multiple-model (VSMM) estimation is the current mainstream tracking algorithm, it possesses high tracking performance but depends largely on the model set used. However, the existing model set design methods still cannot offer higher tracking performance due to the complex maneuverability of the target. The current best model augmentation (BMA) algorithm is an efficient and universal model set design method, but it cannot adapt well to complex maneuvering situations because of its fixed basic and candidate model set. Hence, this paper proposes a modified BMA algorithm for VSMM based on adaptive grid, it can make full use of the previous information to realize an adaptation and time variation of the model set. Overall, two different scenarios were considered, wherein the digital simulation shows that the proposed method can provide a more reliable estimation of the kinematic state and shape of the extended target.

Reliable state estimation for neural networks with TOD protocol and mixed compensation

This paper considers the reliable state estimation issue for discrete-time neural networks with the try-once-discard (TOD) scheduling protocol and mixed compensation strategy. For the phenomenon of medium access constraint, the measurement transmitted from sensors to the estimator is subjected to the TOD scheduling protocol. The mixed compensation is proposed to flexibly compensate those missing measurements caused by the TOD protocol. By using a novel polytopic uncertain model, a reliable state estimator is designed, where the gain matrix is determined by two vertex matrices. Then sufficient conditions are established, which ensure the error system meets the stochastic stability and the l2-l∞ performance. Finally, an illustrative example shows the validity of the proposed reliable state estimator.

State Estimation Fusion for Linear Microgrids over an Unreliable Network

Microgrids should be continuously monitored in order to maintain suitable voltages over time. Microgrids are mainly monitored remotely, and their measurement data transmitted through lossy communication networks are vulnerable to cyberattacks and packet loss. The current study leverages the idea of data fusion to address this problem. Hence, this paper investigates the effects of estimation fusion using various machine-learning (ML) regression methods as data fusion methods by aggregating the distributed Kalman filter (KF)-based state estimates of a linear smart microgrid in order to achieve more accurate and reliable state estimates. This unreliability in measurements is because they are received through a lossy communication network that incorporates packet loss and cyberattacks. In addition to ML regression methods, multi-layer perceptron (MLP) and dependent ordered weighted averaging (DOWA) operators are also employed for further comparisons. The results of simulation on the IEEE 4-bus model validate the effectiveness of the employed ML regression methods through the RMSE, MAE and R-squared indices under the condition of missing and manipulated measurements. In general, the results obtained by the Random Forest regression method were more accurate than those of other methods.

An Online Interval-Based Inertial Navigation System for Control Purposes of Autonomous Boats

Interval analysis is a numerical tool classically used for solving nonlinear equations in a guaranteed way. It has been shown that it can be used to build reliable nonlinear state estimators for dynamical systems. Numerous simulations inspired from real-life applications have shown the applicability of the approach. This paper proposes to implement an interval-based INS (Inertial Navigation System) in an actual robot to estimate its orientation and position. It shows that some types of outliers can be naturally handled by the fusion algorithm, while the resulting controller can be both fast and reliable. Experiments with an actual autonomous boat conclude this article.

An Experimental Urban Case Study with Various Data Sources and a Model for Traffic Estimation.

A reliable estimation of the traffic state in a network is essential, as it is the input of any traffic management strategy. The idea of using the same type of sensors along large networks is not feasible; as a result, data fusion from different sources for the same location should be performed. However, the problem of estimating the traffic state alongside combining input data from multiple sensors is complex for several reasons, such as variable specifications per sensor type, different noise levels, and heterogeneous data inputs. To assess sensor accuracy and propose a fusion methodology, we organized a video measurement campaign in an urban test area in Zurich, Switzerland. The work focuses on capturing traffic conditions regarding traffic flows and travel times. The video measurements are processed (a) manually for ground truth and (b) with an algorithm for license plate recognition. Additional processing of data from established thermal imaging cameras and the Google Distance Matrix allows for evaluating the various sensors’ accuracy and robustness. Finally, we propose an estimation baseline MLR (multiple linear regression) model (5% of ground truth) that is compared to a final MLR model that fuses the 5% sample with conventional loop detector and traffic signal data. The comparison results with the ground truth demonstrate the efficiency and robustness of the proposed assessment and estimation methodology.

LIO-CSI: LiDAR inertial odometry with loop closure combined with semantic information.

Accurate and reliable state estimation and mapping are the foundation of most autonomous driving systems. In recent years, researchers have focused on pose estimation through geometric feature matching. However, most of the works in the literature assume a static scenario. Moreover, a registration based on a geometric feature is vulnerable to the interference of a dynamic object, resulting in a decline of accuracy. With the development of a deep semantic segmentation network, we can conveniently obtain the semantic information from the point cloud in addition to geometric information. Semantic features can be used as an accessory to geometric features that can improve the performance of odometry and loop closure detection. In a more realistic environment, semantic information can filter out dynamic objects in the data, such as pedestrians and vehicles, which lead to information redundancy in generated map and map-based localization failure. In this paper, we propose a method called LiDAR inertial odometry (LIO) with loop closure combined with semantic information (LIO-CSI), which integrates semantic information to facilitate the front-end process as well as loop closure detection. First, we made a local optimization on the semantic labels provided by the Sparse Point-Voxel Neural Architecture Search (SPVNAS) network. The optimized semantic information is combined into the front-end process of tightly-coupled light detection and ranging (LiDAR) inertial odometry via smoothing and mapping (LIO-SAM), which allows us to filter dynamic objects and improve the accuracy of the point cloud registration. Then, we proposed a semantic assisted scan-context method to improve the accuracy and robustness of loop closure detection. The experiments were conducted on an extensively used dataset KITTI and a self-collected dataset on the Jilin University (JLU) campus. The experimental results demonstrate that our method is better than the purely geometric method, especially in dynamic scenarios, and it has a good generalization ability.

State estimation concept for a nonlinear melting/solidification problem of a latent heat thermal energy storage

Latent heat thermal energy storages (LHTES) utilize a material’s phase transition to store energy at an almost constant temperature. To fully exploit their high energy density, reliable state estimation is essential, which requires a suitable model-based observer. In previous works, high-precision and real-time-capable models have been developed to solve the arising coupled Navier-Stokes and energy equations. In the present work, these high-order nonlinear models are applied to predict (simulate) the states of the LHTES one time step ahead. Then, an extended Kalman filter uses a reduced-order observer model derived from the prediction model by linearization and balanced truncation to compute a state update based on measurements. This approach increases both, computational efficiency and performance, since the observer can only update the state of the prediction model compliant to its dominant behavior. Different types of measurements can be accurately combined in the observer, resulting in fast convergence despite model errors.

Adaptive cubature Kalman filter with the estimation of correlation between multiplicative noise and additive measurement noise

Mobile robots are often subject to multiplicative noise in the target tracking tasks, where the multiplicative measurement noise is correlated with additive measurement noise. In this paper, first, a correlation multiplicative measurement noise model is established. It is able to more accurately represent the measurement error caused by the distance sensor dependence state. Then, the estimated performance mismatch problem of Cubature Kalman Filter (CKF) under multiplicative noise is analyzed. An improved Gaussian filter algorithm is introduced to help obtain the CKF algorithm with correlated multiplicative noise. In practice, the model parameters are unknown or inaccurate, especially the correlation of noise is difficult to obtain, which can lead to a decrease in filtering accuracy or even divergence. To address this, an adaptive CKF algorithm is further provided to achieve reliable state estimation for the unknown noise correlation coefficient and thus the application of the CKF algorithm is extended. Finally, the estimated performance is analyzed theoretically, and the simulation study is conducted to validate the effectiveness of the proposed algorithm.

Machine learning pipeline for battery state-of-health estimation

Lithium-ion batteries are ubiquitous in applications ranging from portable electronics to electric vehicles. Irrespective of the application, reliable real-time estimation of battery state of health (SOH) by on-board computers is crucial to the safe operation of the battery, ultimately safeguarding asset integrity. In this Article, we design and evaluate a machine learning pipeline for estimation of battery capacity fade—a metric of battery health—on 179 cells cycled under various conditions. The pipeline estimates battery SOH with an associated confidence interval by using two parametric and two non-parametric algorithms. Using segments of charge voltage and current curves, the pipeline engineers 30 features, performs automatic feature selection and calibrates the algorithms. When deployed on cells operated under the fast-charging protocol, the best model achieves a root-mean-squared error of 0.45%. This work provides insights into the design of scalable data-driven models for battery SOH estimation, emphasizing the value of confidence bounds around the prediction. The pipeline methodology combines experimental data with machine learning modelling and could be applied to other critical components that require real-time estimation of SOH.

An Improved Adaptive Unscented FastSLAM with Genetic Resampling

The simultaneous localization and mapping (SLAM) is a significant topic in intelligent robot. In this paper, an improved adaptive unscented FastSLAM with genetic resampling is proposed. Specifically, the adaptive unscented Kalman filter (IAUKF) algorithm is improved as importance sampling of particle filter, where the adaptive factor is used to improve the tracking ability of system and the Huber cost function is constructed to decrease the measurement covariance. Next, the process noise and the measurement noise are assessed by a time varying estimator. Moreover, the resampling in particle filter is carried out by an improved genetic algorithm (GA). Finally, the improved adaptive unscented FastSLAM (IAUFastSLAM) is proposed to complete robot tracking. The proposed algorithm has good tracking performance and obtains reliable state estimation in SLAM. Simulation results reveal the validity of the proposed algorithm.