State Estimation Methodology Research Articles

Harmonic State Estimation in Power Systems Using the Jaya Algorithm

The increasing use of nonlinear loads in power systems introduces voltage and current components at non-fundamental frequencies, leading to harmonic distortion, which negatively impacts electrical and electronic devices. A common mitigation strategy involves identifying harmonic sources and installing filters nearby. However, due to the high cost of power quality (PQ) meters, comprehensive harmonic level monitoring across the entire power system is impractical. To address this, various methodologies for Harmonic State Estimation (HSE) have been developed, which estimate distortion levels on unmonitored system buses using data from a minimal set of monitored ones. Many HSE techniques rely on optimization algorithms with numerous tuning parameters, complicating their application. This paper proposes a novel methodology for fundamental frequency power flow and harmonic state estimation using the Jaya algorithm, which is characterized by fewer tuning parameters for easier adjustment. It also introduces a strategy to determine the minimal number of buses that need monitoring to achieve system observability. The methodology is validated on the IEEE-14 and IEEE-30 bus systems, demonstrating its effectiveness. The results of the proposed methodology are compared with those obtained using Evolutionary Strategies (ESs), highlighting its enhanced accuracy and computational efficiency.

Cluster-based Bayesian approach for noisy and sparse data: application to flow-state estimation

This study presents a cluster-based Bayesian methodology for state estimation under realistic conditions including noisy data from sparse sensors. The proposed approach is interpretable and, building upon previous work on transition networks, explicitly accounts for experimental noise within the data-driven framework by means of data clustering. Experimental measurements are exploited, beyond model training, to quantify the degree of uncertainty (noise) for each trained state. Such noise levels are eventually associated with probability distributions that, when combined with Bayes’ theorem, allow us to perform real-time state estimation. The proposed methodology is tested on two cases of challenging flows generated by an accelerating elliptical plate and also a delta wing experiencing gusts. Results specifically indicate that the proposed approach is robust against the number of clusters, enabling state estimation with a significant order reduction, notably decreasing the computational cost while preserving estimation accuracy. Based on the present findings, the proposed data-driven approach can be employed for realistic state estimation in nonlinear systems where noise, sensor sparsity and nonlinearities represent a challenging scenario.

A novel state estimation method for distribution networks with low observability based on linear AC optimal power flow model

The state estimation (SE) problem is one of the key functions in real-time control and management of modern electrical distribution networks. However, due to the complex network configuration and the limited number of measuring devices in these networks, the SE problem faces many challenges. In this manuscript, a new state estimation methodology is proposed based on the AC optimal power flow (ACOPF) model for distribution networks with a small number of real-time measurements. The proposed method is formulated in the form of a mixed integer linear programming problem (MILP), where the estimated states are regarded as the decision variables in the optimization problem. The load consumption values are also assumed as the decision variables where the estimation limits of these variables are determined by pseudo-measurements taken from the historical data. The main objective function is to maximize the state estimation accuracy, while the power flow equations are defined as the problem constraints. The efficiency of the proposed methodology is evaluated by implementing on the test networks and comparing the estimation results accuracy with the conventional weighted least squares (WLS) method. The obtained numerical results show the superior performance of OPF-based SE model to WLS method. Moreover, linearizing the formulation demonstrates an acceptable precision while ensuring convergence and optimum solution in low observability conditions.

A KRR-UKF robust state estimation method for distribution networks

State estimation is an integral component of energy management systems. Employing a state estimation methodology that is both accurate and resilient is essential for facilitating informed decision-making processes. However, the complex scenarios (unknown noise, low data redundancy, and reconfiguration) of the distribution network pose new challenges for state estimation. In the context of this study, we introduce a state estimation technique known as the kernel ridge regression and unscented Kalman filter. In normal conditions, the non-linear correlation among data and unknown noise increases the difficulty of modeling the distribution network. Thence, kernel ridge regression is developed to map the data into high-dimensional space that transforms the non-linear problem into linear formulations base on the data rather the complicate grid model, which improves model generalization performance and filters out unknown noises. In addition, with the unique prediction correction mechanism of the Kalman method, the kernel ridge regression-mapped state value can be revised by the measurement, which further enhances model accuracy and robustness. During abnormal operating conditions and taking into account the presence of faulty data within the measurement system, we initiate the use of a long short-term memory network and combined convolutional neural network (CNN) model, referred to as the ATT-CNN-GRU. This model is utilized for the prediction of pseudo-measurements. Subsequently, we use an outlier detection method known as ordering points to identify the clustering structure to effectively identify and substitute erroneous data points. Cases on the IEEE-33 bus system and 109-bus system from a city in China show that the method has superior accuracy and robustness.

Cloud-Battery management system based health-aware battery fast charging architecture using error-correction strategy for electric vehicles

The health-aware battery fast charging (HABFC) strategy for electric vehicles (EVs) is predominantly dependent on the input conditions. These conditions are set using a set of various type of sensors (temperature, current, voltage, etc.), which can seldom be erroneous. Also, one of the primary challenges associated with physical battery management system (BMS) is the lack of memory required to execute intricate state estimation methodologies. Therefore, this paper proposes the Cloud-BMS based health-aware battery fast charging (HABFC) architecture with error correction strategy for reducing the charging time and increasing the cycle life of the EV battery. A cloud BMS has been integrated in the HABFC architecture to reduce the dependency on erroneous sensors. To design an effective cloud BMS, this paper reviews the seven most critical battery state estimation techniques (state of charge (SoC), state of health (SoH), state of power (SoP), state of energy (SoE), state of temperature (SoT), state of function (SoF), and state of safety (SoS)). Based on comparison of mathematical computational methods, its accuracy and challenges, the best computational method for each state estimation technique has been determined. The cloud BMS makes all state estimations based on parameters estimated using the virtual battery. Further, this paper presents an error correction strategy that helps correct the error in physical BMS employing a master-slave configuration. The proposed cloud BMS based HABFC architecture is validated using a cell cycling setup. It can be concluded that the proposed architecture produces 21.24% more cycle life as compared to fast charging without HABFC and 11% more cycle life as compared to cell charged using HABFC without cloud BMS.

Fractional order PI observer for a class of fractional order systems

Currently the study of fractional order systems has become of great research interest, in particular the state estimation stands out within the lines of studies for this type of systems. Different methodologies have been proposed in order to solve this problem, however most of the techniques involve complete information of the system. Thus, this work presents a methodology for state estimation in a class of fractional order systems based on a fractional order observer which is constructed through an algebraic technique. This observer presents some significant properties, for instance, only variables of interest are estimated, i.e., it is a non redundant observer, it does not need complete information of the system and the initial conditions for the observer can be freely assigned. The stability of this observer is analyzed with the global Mittag-Leffler boundedness approach. Finally, the effectiveness and accuracy of the proposed methodology is verified through state estimation of a fractional-order Duffing chaotic system.

Multi-pass Extended Kalman Smoother with Partially-known Constraints for Estimation of Vapor Compression Cycles

State and parameter estimation methodologies have the potential to make a significant impact in the development of broad array of capabilities for widely-used vapor compression cycles, including advanced controls, performance monitoring, data-driven modeling, and deployment of digital twin technologies. However, the nonlinearity and numerical stiffness of large physics-based models of these systems pose challenges for the practical implementation of estimators that must also satisfy the physical state constraints. We present a three-pass fixed-interval smoothing method developed in the extended Kalman estimation formalism that incorporates linear inequality and partially-known nonlinear equality constraints defined in terms of unknown parameters of the system. The smoothing method is demonstrated to have high estimation accuracy during joint state and parameter estimation of the cycle model representing a realistic system that is implemented in Julia language leveraging automatic differentiation capabilities.

Sensor Fault-Tolerant State Estimation by Networks of Distributed Observers

We propose a state estimation methodology using a network of distributed observers. We consider a scenario in which the local measurement at each node may not guarantee the system's observability. In contrast, the ensemble of all the measurements does ensure that the observability property holds. As a result, we design a network of observers such that the estimated state vector computed by each observer converges to the system's state vector by using the local measurement and the communicated estimates of a subset of observers in its neighborhood. The proposed estimation scheme exploits sensor redundancy to provide robustness against faults in the sensors. Under suitable conditions on the redundant sensors, we show that it is possible to mitigate the effects of a class of sensor faults on the state estimation. Simulation trials demonstrate the effectiveness of the proposed distributed estimation scheme.

Non-destructive preservation state estimation of waterlogged archaeological wooden artifacts

Non-destructive preservation state estimation is an essential prerequisite for the preservation and conservation of waterlogged archaeological wooden artifacts. Herein, Near Infrared (NIR) spectroscopy coupled with orthogonal partial least squares discriminant analysis (OPLS-DA) were applied to assess sixty-four waterlogged archaeological woods collected from seven excavation sites in the period range of 2900 BCE-1912 CE, aiming at developing a non-destructive, accurate and rapid preservation state estimation methodology. The role of non-decayed recent wood of relevant species on preservation state estimation was studied in prior, showing the use of non-decayed recent wood could not improve the predictive ability. Besides, the high variability in terms of chemical structure between archaeological softwoods and archaeological hardwoods did affect the preservation state estimation. Thus, a simple OPLS-DA model of non-destructively distinguishing archaeological hardwoods from softwoods, R2Xcum of 0.659, R2Ycum of 0.836 and Q2cum of 0.763, was established to avoid and overcome destructive approach for wood identification. Then, the well-defined three grouped separations of slightly-decayed, moderately-decayed and severely-decayed waterlogged archaeological woods were revealed in OPLS-DA models, providing R2Xcum of 0.793, R2Ycum of 0.738, Q2cum of 0.680, and R2Xcum of 0.780, R2Ycum of 0.901, Q2cum of 0.870, for waterlogged archaeological hardwoods and waterlogged archaeological softwoods respectively. Potential predictive wood spectral bands were screened and tentatively identified as hydroxyls of crystalline cellulose, acetyl groups of hemicelluloses, C-H bands of lignin, which guaranteed the elimination of non-structural compounds, such as water and inorganic components interference. Furthermore, the developed NIR methodology was validated by an extensively used destructive method consisting of anatomical characteristics, maximum water content and basic density analyses. The results indicated that NIR coupled to chemometrics could non-destructively and accurately predict the preservation states of waterlogged archaeological wooden artifacts and avoid the interference of water and inorganic deposits.

Robust Dynamic State Estimator of Integrated Energy Systems Based on Natural Gas Partial Differential Equations

The reliability and precision of dynamic database are vital for the optimal operating and global control of integrated energy systems. One of the effective ways to obtain the accurate states is state estimations. A novel robust dynamic state estimation methodology for integrated natural gas and electric power systems is proposed based on Kalman filter. To take full advantage of measurement redundancies and predictions for enhancing the estimating accuracy, the dynamic state estimation model coupling gas and power systems by gas turbine units is established. The exponential smoothing technique and gas physical model are integrated in Kalman filter. Additionally, the time-varying scalar matrix is proposed to conquer bad data in Kalman filter algorithm. The proposed method is applied to an integrated gas and power systems formed by GasLib-40 and IEEE 39-bus system with five gas turbine units. The simulating results show that the method can obtain the accurate dynamic states under three different measurement error conditions, and the filtering performance are better than separate estimation methods. Additionally, the proposed method is robust when the measurements experience bad data.

A Digital Twin of a Water Distribution System by Using Graph Convolutional Networks for Pump Speed-Based State Estimation

Water distribution system monitoring is currently carried out using advanced real-time control technologies to achieve a higher operational efficiency. Data analysis techniques can be implemented for condition estimation, which are crucial tools for managing, developing, and operating water networks using the monitored flow rate and pressure data at some network pipes and nodes. This work proposes a state estimation methodology that enables one to infer the hydraulic state of the operating speed of pumping systems from these pressure and flow measurements. The presented approach suggests using graph convolutional neural network theory linked to hydraulic models for generating a digital twin of the water system. It is validated on two benchmark hydraulic networks: the Patios-Villa del Rosario, Colombia, and the C-Town networks. The results show that the proposed model effectively predicts the state estimation in the two hydraulic networks used. The results of the evaluation metrics indicate low values of mean squared error and mean absolute error and high values of the coefficient of determination, reflecting high predictive ability and that the prediction results adequately represent the real data.

Harmonic state estimation based on graph signal processing

This paper presents a novel harmonic state estimation (HSE) methodology based on graph signal processing. The proposed approach can perform HSE without calculating harmonic impedance. The proposed methodology involves graph signal theory for constructing a graph Laplacian matrix and an unconstrained optimization function, the adaptive K-means algorithm for building a prior information model of the target bus, as well as data fuzzification and the domain averaging method for state estimation. Simulations are carried out using a 30-bus power distribution system, and results are presented and discussed to prove the efficiency of the proposed method. The practical application further verifies the availability of the proposed method.

Bayesian Traffic State Estimation Using Extended Floating Car Data

Traffic state estimation is a challenging task due to the collection of sparse and noisy measurements from specific points of the traffic network. The emergence of Connected and Automated Vehicles (CAVs) provides new capabilities for traffic state estimation using extended floating car data such as position, speed and spacing information. In this work we propose a Bayesian Traffic State Estimation (BTSE) methodology for estimating the traffic density based on extended floating car data. BTSE utilizes the Bayesian paradigm to express any prior information to derive probability distributions of the traffic density of different road segments of the traffic network. Two variations of the BTSE methodology are developed to handle the offline and online estimation problem. The BTSE methodology is evaluated both using realistic SUMO micro-simulations for M25 Highway, London, U.K., and a real-life vehicle-trajectory dataset from German highways, extracted from videos recorded by drones. The efficiency and accuracy of the BTSE methodology is compared to an existing methodology in the literature. We present results for the estimation performance of the methods showing that the Bayesian methodology consistently results in lower mean absolute percentage error than the compared literature method. The BTSE methodology yields high-quality estimation results even for a low penetration rate of CAVs (e.g. 5%).

A methodology to improve simulation of multibody systems using estimation techniques

ABSTRACT This paper presents a methodology for state estimation and accuracy improvement of computer simulations of computer aided engineering (CAE) models based on prediction and correction state estimation techniques and sensing. The aim is to simulate the dynamic behaviour of a real system, which can be sensed, and obtain values of states that are not measurable due to economic or technical limitations. This methodology can be applied to both optimization of design processes and on-line control of complex systems. State estimation techniques are currently used only on mathematical models, where the relationships among system variables are expressed by means of mathematical language, making state observer implementation possible but leading to limitations in system modelling and knowledge. Favoured over mathematical models, multibody CAE models (created by means of computer-aided engineering software) have become the essential tool for complex system development, simulation, analysis, optimization and control, such as multibody systems; one of their main advantages is the ease and flexibility in creating and modifying them, allowing the faithful modelling of complex systems.

A Modified Unscented Kalman Filter Combined with Ant Lion Optimization for Vehicle State Estimation

Accurate estimation of vehicle states is extremely crucial for vehicle stability control. As a reliable estimation methodology, the unscented Kalman filter (UKF) has been widely utilized in vehicle control. However, the estimation accuracy still needs to be improved caused by the unpredictable measurement and process noise. In this paper, a novel modified UKF state estimation methodology combined with the ant lion optimization (ALO) is proposed for the stability control of a four in-wheel motor independent drive electric vehicle (4WIDEV). First, the optimal performance of the ALO algorithm is analyzed, where both unimodal and multimodal optimization test functions are selected and optimized by GA, PSO, and ALO, respectively. The results indicate that the ALO algorithm has good global optimization capability and applicability. Second, the ALO algorithm is merged into the UKF to adjust the statistical properties of noise information for the ALOUKF estimator design without extra sensor signals. At last, the simulations on the Matlab/Simulink-CarSim co-simulation platform and the road test based on an A&D 5435 rapid prototyping experiment platform (RPP) are carried out to verify the proposed method. The simulation and experiment results demonstrate that the ALOUKF estimator can improve state estimation accuracy and resist the vehicle nonlinearity even in the case of the complicated and emergency maneuvers.

Nonlinear Least Absolute Value Estimator for Topology Error Detection and Robust State Estimation

Topology error, a modeling misrepresentation of the power system network configuration, can undermine the quality of state estimation. In this paper, we propose a new methodology for robust power system state estimation (PSSE) modeled by AC power flow equations when there exists a small number of topological errors. The developed technique utilizes the availability of a large number of SCADA measurements and minimizes the $\ell _{1}$ norm of nonconvex residuals augmented by a nonlinear, but convex, regularizer. Representing the power network by a graph, we first study the properties of the solution obtained from the proposed NLAV estimator and demonstrate that, under mild conditions, this solution identifies a small subgraph of the network that contains the topological errors in the model used for the state estimation problem. Then, we introduce a method that can efficiently detect the topological errors by searching over the identified subgraph. In addition, we develop a theoretical upper bound on the state estimation error to guarantee the accuracy of the proposed state estimation technique. The efficacy of the developed framework is demonstrated through numerical simulations on IEEE benchmark systems.

Evaluation of lightweight battery management system with field test of electric bus in campus transit system

A battery management system is a crucial part of a battery-powered electric vehicle, which functions as a monitoring system, state estimation, and protection for the vehicle. Among these functions, the state estimation, i.e., state of charge and remaining battery life estimation, is widely researched in order to find an accuracy estimation methodology. Most of the recent researches are based on the study of the battery cell level and the complex algorithm. In practice, there is a statement that the method should be simple and robust. Therefore, this research work is focused on the study of lightweight methodology for state estimation based on the battery pack. The discrete Coulomb counting method and the data-driven approach, based on the Palmgren-Miner method, are proposed for the estimation of the state of charge and remaining battery life, respectively. The proposed methods are evaluated through a battery-powered electric bus under real scenario-based circumstances in the campus transit system. In addition, the battery life-cycle cost analysis is also investigated. The tested bus has currently been in operation in the transit system for more than 
 one year.

HANDS: a multimodal dataset for modeling toward human grasp intent inference in prosthetic hands.

Upper limb and hand functionality is critical to many activities of daily living and the amputation of one can lead to significant functionality loss for individuals. From this perspective, advanced prosthetic hands of the future are anticipated to benefit from improved shared control between a robotic hand and its human user, but more importantly from the improved capability to infer human intent from multimodal sensor data to provide the robotic hand perception abilities regarding the operational context. Such multimodal sensor data may include various environment sensors including vision, as well as human physiology and behavior sensors including electromyography and inertial measurement units. A fusion methodology for environmental state and human intent estimation can combine these sources of evidence in order to help prosthetic hand motion planning and control. In this paper, we present a dataset of this type that was gathered with the anticipation of cameras being built into prosthetic hands, and computer vision methods will need to assess this hand-view visual evidence in order to estimate human intent. Specifically, paired images from human eye-view and hand-view of various objects placed at different orientations have been captured at the initial state of grasping trials, followed by paired video, EMG and IMU from the arm of the human during a grasp, lift, put-down, and retract style trial structure. For each trial, based on eye-view images of the scene showing the hand and object on a table, multiple humans were asked to sort in decreasing order of preference, five grasp types appropriate for the object in its given configuration relative to the hand. The potential utility of paired eye-view and hand-view images was illustrated by training a convolutional neural network to process hand-view images in order to predict eye-view labels assigned by humans.

Constrained ensemble Kalman filter based on Kullback–Leibler divergence

Conventional recursive state estimation procedures cannot handle inequality constraints; therefore, they might result in physically meaningless state estimates. In this work, we develop a novel state estimation technique to incorporate inequality constraints in ensemble Kalman filters (EnKF). For this purpose, we first solve for the unconstrained EnKF state estimation, and then construct a constrained distribution based on the unconstrained one by formulating an optimization problem using the Kullback–Leibler (KL) divergence. The proposed constraint implementation technique is cast as a convex optimization problem involving second order cone constraints which can be solved to global optimality. This constraint implementation step can be integrated into any Gaussian filter. Demonstrative case studies of chemical reaction systems are presented to compare and illustrate the efficacy of the proposed state estimation methodology.

Solving Ill-Conditioned State-Estimation Problems in Distribution Grids With Hidden-Markov Models of Load Dynamics

Future smart grid operations must rely upon accurate state estimation (SE). Estimation at the distribution level is very challenging since the customary monitoring infrastructure inhibits access to real-time widespread information, precluding redundancy. To overcome the lack of redundancy, we propose to use Markov information on load dynamics instead of load pseudo-measurements directly. The proposed SE methodology Combines a (i) regularization-based method for solving ill-conditioned load-estimation problems with (ii) a Markov model for restricting load variations to probable time-varying load changes. Two Markov approaches are implemented. First, Markov information is used to estimate load one-step ahead, allowing a simple forward dynamic estimation. Then, the SE problem is formulated as a Hidden Markov problem and solved with the Viterbi algorithm to estimate load evolution. Several monitoring contexts are finally analyzed to illustrate the approaches proposed and to discuss the limitations of their application in realistic contexts.