State Estimation Model Research Articles

A High Dimensional and High Sigma Statistical Model for SRAM Read Access Yield Estimation

A High Dimensional and High Sigma Statistical Model for SRAM Read Access Yield Estimation

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.

Stealthy attack detection based on controlled invariant subspace for autonomous vehicles

Autonomous driving offers a convenient and comfortable means of transportation, while the addition of external communication interfaces renders the vehicle communication more vulnerable to cyber attacks. In contrast to conventional attacks, well-designed stealthy attacks are more adept at evading conventional detectors. To address this problem, this paper proposes a robust detector based on controlled invariant subspace to identify stealthy attacks. This paper stands out not only by relying on the vehicle dynamics model for system state estimation, but rather by employing a real-time trajectory space generation method. Our detector develops a rapidly expanding random tree method based on offline incremental learning to generate trajectory subspace, and any trajectories that diverge from this subspace are identified as anomalies. Additionally, this paper designs distance measurement method that combines the linear quadratic regulation algorithm with a radial basis function neural network while also taking into account the vehicle's dynamic performance constraints. To assess the theoretical performance of the proposed detector, this paper presents rigorous proofs that demonstrate the asymptotical optimality of the generated trajectory and the robustness of the stealthy attack detection method based on controlled invariant subspace. Finally, simulation results demonstrate that the proposed robust detector can effectively identify stealthy attacks and outperforms advanced techniques in terms of accuracy and false-positive rate.

A Novel Class-Imbalanced Ship Motion Data-Based Cross-Scale Model for Sea State Estimation

A Novel Class-Imbalanced Ship Motion Data-Based Cross-Scale Model for Sea State Estimation

Incorporating Elicited Preferences for Equality into Electricity System Planning Modeling

Sustainable Development Goal 7 of the United Nations is to achieve universal access to clean, modern and affordable electricity by 2030. However, 600 million people in sub-Saharan Africa (SSA) currently do not have access to electricity. As a result of this energy inequality, countries in SSA need to plan electricity systems that provide access in an equitable manner. The research question we explore in this paper is how integrating elicited preferences for equality into an electricity system planning model affects investment decisions regarding technology deployment. Our novel contribution is proposing a framework in the form of a discrete choice experiment and a statistical estimation model to determine decision makers’ preferences for equality. In our study, we find that higher preferences for equality result in an increased deployment of solar diesel mini-grids. These hybrid mini-grids, in turn, drive the carbon emissions intensity of the electricity system fourfold. As such, there is a need for stakeholders in Africa’s energy sector to consider the potential divergence between a carbon-minimizing electrification strategy and equitable electrification.

Multi-area state estimation for active distribution networks under multiple uncertainties: An affine approach

An enormous challenge for the state estimation (SE) process in large-scale distribution networks lies in how to deal with multiple uncertainties and the increasing computational tasks simultaneously. This paper aims to address the issue and propose a multi-area architecture for perceiving system status with uncertain inputs. The first step involves the introduction of a novel Louvain-flow-based partition structure, which effectively divides large-scale unbalanced distribution networks into several sub-areas. Subsequently, an affine state estimation (ASE) model, employing the Neumann series method, is formulated for each sub-area, taking into account uncertainties in system measurements and line parameters. Finally, the interior-point filter line-search algorithm combined with consensus theory is utilized to solve all local ASE models, and the interaction of boundary state between adjacent sub-areas is focused and completed, to output the global state results of the distribution network precisely. Case studies are presented to verify that the proposed distributed ASE model yields obvious advantages in tracking system uncertainties by contrast with the existing SE model.

Tensile loading state sensing and estimation of opaque polymer materials using pulsed-terahertz waves

In this study, we used pulsed terahertz (THz) spectroscopy to analyze the THz optical properties of opaque acrylonitrile–butadiene–styrene (ABS). The phenomenon occurring upon the propagation of pulsed THz waves through ABS was analyzed through measurements of the variations in THz optical parameters (absorption constant, complex dielectric properties, and refractive index). When tensile loading of 0.1–1.5 kN was applied to a specimen, large optical coefficient variations were observed at 0.275, 0.471, and 0.686 THz, regardless of the applied tensile loading. The optical parameter variations were analyzed at 0.275 THz. Moreover, the accuracy of a tensile loading state estimation model established using principal component analysis was analyzed; consequently, random loading was confirmed. The input factor was extracted and calculated, with an error of approximately 3%, except for small loading (100 N).

State-space modeling for electrochemical performance of Li-ion batteries with physics-informed deep operator networks

Online estimation of unobservable internal states is significant for safe operation of Li-ion batteries, and it constitutes one of the main functions of battery management system (BMS). The next-generation BMS expects model-based state estimation, especially with electrochemical models, which are accurate but often costly for solving. Therefore, it is required to build more easily executable state-space representation of electrochemical models for online state estimation. However, the traditional numerical methods for time discretization are relatively complicated, and the discretized system is not very flexible in modifying predictive time intervals. To address such issues, we introduce the concept of physics-informed operator learning for state-space modeling. Specifically, we propose an architecture, termed the physics-informed multiple-input operator network (PI-MIONet), to reformulate the state-space representation of the extended single particle (eSP) model. In this work, the PI-MIONet takes the Li-ion concentration of the whole electrode particle and current densities at the current time as the input functions, and predicts Li-ion concentration at any spatial-temporal location, which means that the forward predictions can be realized with user-defined step size. In addition, due to the capability of taking discretized functions as inputs, the PI-MIONet can be used for estimating states in the form of long vectors, and it can be conducted very efficiently, which makes it highly suitable for online applications in BMS. We verify the predictive performance of PI-MIONet through several synthetic experiments, and successfully apply it to the estimation of Li-ion concentration across the full particle with unscented Kalman filter algorithms.

Distributed security state estimation-based carbon emissions and economic cost analysis for cyber–physical power systems under hybrid attacks

Sustainable cyber–physical power systems (CPPSs) significantly reduce carbon emissions due to climate change. However, when the data exchange in CPPSs suffers from hybrid attacks, the distributed state estimation and optimal power flow (OPF) analysis will inevitably be compromised, leading to inadequate or faulty scheduling of clean energy and thermal power generations and further affecting the total carbon emissions and economic cost. To address these problems, this paper proposes a novel consensus-based distributed security state estimation (DSSE) method for CPPSs, which is used to analyze the impact of hybrid attacks on carbon emissions and economic cost. Firstly, the incomplete and non-authentic data features caused by hybrid attacks are described, and their influence on distributed state estimation model is analyzed. A new residual-based attack detection method is then constructed in each subregion, where secure and non-secure sets are employed to describe whether the subregion is attacked and the compensation mechanism is designed for the non-secure set. Secondly, considering data compensation, distributed state estimation model is reconstructed, and a distributed security state estimation method under hybrid attacks is proposed while its convergence condition is derived. Thirdly, the impacts of hybrid attacks on carbon emissions and economic costs are analyzed based on the proposed DSSE method. Finally, experimental results confirm the validity of the theoretic analysis.

Machine learning and UAV path following identification algorithm based on navigation spoofing

With advances in navigation technology, unmanned aerial vehicles (UAVs) have become widely available to all sections of society. Given the potential hazards of UAVs, as seen from their use in the Russia–Ukraine war and security incidents at London and Baghdad airports, counter-UAV technology is receiving unprecedented attention. This paper describes a method for taking control of a UAV indirectly by navigation spoofing and luring it to a designated area from which it can be neutralized. Our contributions are threefold: first, we analyze the limitations of the traditional state estimation and control model for UAV navigation based on the integration of the Global Navigation Satellite System and Inertial Navigation System. Second, we propose a particle hypothesis and planning model that is insensitive to the particular UAV navigation system, and decompose the UAV navigation spoofing process into two steps: identification and planning. Finally, to overcome the poor heading angle prediction accuracy of unary polynomial regression, we propose a support vector regression algorithm that improves the prediction accuracy from 1.5° to 1.01° under navigation spoofing. Experimental results using the proposed navigation spoofing method prove that machine learning offers significant advantages in modeless identification.

Deep Learning-Enabled Statistical Model Estimation for Power Transformers with Censoring and Truncation Problems

Traditional statistical models, e.g., Weibull distributions, are popular solutions for failure modeling and degradation anal- ysis in a variety of industries. To estimate the parameters of these statistical models, maximum likelihood estimation (MLE) is often engaged through various optimization algo- rithms. However, when dealing with highly reliable or new equipment, it is challenging to fit limited or unbalanced data to obtain an accurate model. In this paper, we propose a deep learning (DL)-based model for estimating the Weibull param- eters with both censoring and truncation problems. Instead of using the conventional matrices such as concordance index, we propose a novel validation framework to examine the pre- diction accuracy of different models. We examine the perfor- mance of the proposed approach on real-world power trans- former data, and the results show that our approach can im- prove prediction accuracy and is less susceptible to the trun- cation problem. Our results also suggest that deep learning techniques can help enhance traditional statistical modeling for reliability analysis.

Using (α, xn) reaction rates and abundance ratios to constrain the weak r-process

The lighter heavy elements of the first r-process peak, between strontium and silver, can be synthesized in the moderately neutron-rich neutrino–driven ejecta of either core–collapse supernovae or neutron star mergers via the weak r–process. This nucleosynthesis scenario exhibits uncertainties from the absence of experimental data from (α, xn) reactions on neutron–rich nuclei, which are currently based on statistical model estimates. We have performed a new impact study to identify the most important (α, xn) reactions that can affect the production of the lighter heavy elements under different astrophysical conditions and using new, constrained (α, xn) reaction rates based on the Atomki-V2 αOMP. We have identified a list of relevant reactions that affect elemental abundance ratios that can be compared to abundances from metal-poor stars. Our results show how when reducing the nuclear physics uncertainties, we can use abundance ratios to constrain the astrophysical conditions/environment. This will be possible with the planned experiments to measure key (α, xn) reaction rates using the SECAR recoil separator at FRIB that will also be briefly discussed.

A False Data Injection Attack Detection Strategy for Unbalanced Distribution Networks State Estimation

With the advance in communication facilities and information technologies, the state estimation (SE) of distribution networks is subject to intensified cybersecurity threats caused by false data injection attacks (FDIAs). To address the issues, this paper proposes a novel FDIA detection strategy for unbalanced distribution networks. The SE model and corresponding general imperfect FDIA are introduced first to emulate the attacking behavior in practice. To achieve FDIA detection, we propose a square-root unscented Kalman filter (SR-UKF) based forecasting-aided SE (FASE) to generate estimation results. By modifying the filtering step of the proposed FASE into a redundant linear regression form, random outliers can be effectively detected and suppressed by leveraging the projection statistics (PS). Afterward, based on the acquired SE results, a generalized likelihood ratio test (GLRT) is designed to detect FDIAs on consecutive snapshots. In the GLRT, the dynamic time warping (DTW) distance between two innovation sequences is set as the test variable, which is compared with the offline determined detection threshold under a specific false alarm rate. The feasibility of the proposed general imperfect FDIA and the effectiveness of the proposed FDIA detection strategy are validated through extensive numerical simulations.

Multi-source-data state estimation for integrated electricity–heat-building networks

Building heating systems (BHS) play a major role in integrated electricity and heat systems (IEHS). To capture the real-time dynamic operating state of IEHS based on multi-source measurement data, this paper develops a comprehensive state estimation (SE) framework for IEHS with BHS fleets. First, a thermo-electrical SE model integrating BHS, electric power grid (EPG), and district heating network (DHN) is developed. Then, to jointly estimate the state variables of IEHS with different time scales of dynamics, a holistic SE algorithm is proposed based on the partial equivalence between the Weighted Least Squares (WLS) estimation problem and the correction stage of the Iterative Extended-Kalman Filter (IEKF). It also includes a data anomaly processing algorithm to combat data corruptions from sensing and communication processes. Simulation results show that the proposed framework can accurately track the thermo-electrical states of the system both in the presence of measurement noise and anomalies during various dynamics in IEHS.

A non-intrusive carbon emission accounting method for industrial corporations from the perspective of modern power systems

Accurate and timely carbon emission accounting (CEA) is vital to industrial corporations, especially those who participate in the carbon market. With the rapid development of artificial intelligence and power systems, the power data-based method provides a new way for real-time CEA. However, the extensive installation of distributed photovoltaics (PV) significantly increases the accounting difficulty of corporate carbon emissions. This paper proposes a non-intrusive method of real-time CEA for industrial corporations from the perspective of modern power systems. First, a device operation state (DOS) estimation model based on a modified Informer algorithm is proposed to calculate corporate direct carbon emissions. Wherein, an equivalent distributed PV output estimation model is used to decrease the impact of invisible PVs on direct emission accounting. Second, an improved carbon emission flow model is proposed to calculate corporate indirect carbon emissions, which considers “prosumers” arising from the installation of distributed PVs. Finally, the total corporate carbon emissions, including direct and indirect parts, are obtained by using the CEA model. Case studies based on four typical high‑carbon-emission factories in Zhejiang province, China demonstrate that the proposed method can make accurate CEA for industrial corporations by effectively lessening the impact of distributed PVs.

Estimation of Time-Series Forest Leaf Area Index (LAI) Based on Sentinel-2 and MODIS

The LAI is a key parameter used to describe the exchange of material and energy between soil, vegetation and the atmosphere. It has become an important driving datum in the study of carbon and water cycle mechanism models at many regional scales. In order to obtain high temporal resolution and high spatial resolution LAI products, this study proposed a method to combine the high temporal resolution of MODIS LAI products with the high spatial resolution of Sentinel-2 data. The method first used the LACC algorithm to smooth the LAI time-series data and extracted the normalized growth curve of the MODIS LAI of forest and used this curve to simulate the annual variation of the LAI. Secondly, it estimated the LAI at the period of full leaf spread based on the traditional remote sensing statistical model and Sentinel-2 remote sensing data as the maximum value of the forest LAI in the study area and used it to control the LAI growth curve. Finally, the time-series LAI data set was created by multiplying the maximum LAI by the normalized forest LAI growth curve. The results indicate that: (1) the remote sensing statistical estimation model of LAI was developed using the atmospherically resistant vegetation index ARVI (R2 = 0.494); (2) the MODIS LAI normalized growth curve keeps a good level of agreement with the actual variation. This study provides a simple and efficient method for obtaining effective time-series forest LAI data for the scope of small- and medium-sized areas.

Rendezvous and Proximity Operations in Cislunar Space Using Linearized Dynamics for Estimation

As interest in Moon exploration grows, and efforts to establish an orbiting outpost intensify, accurate modeling of spacecraft dynamics in cislunar space is becoming increasingly important. Contrary to satellites in Low Earth Orbit (LEO), where it takes around 5 ms to communicate back and forth with a ground station, it can take up to 2.4 s to communicate with satellites near the Moon. This delay in communication can make the difference between a successful docking and a catastrophic collision for a remotely controlled satellite. Moreover, due to the unstable nature of trajectories in cislunar space, it is necessary to design spacecraft that can autonomously make frequent maneuvers to stay on track with a reference orbit. The communication delay and unstable trajectories are exactly why autonomous navigation is critical for proximity operations and rendezvous and docking missions in cislunar space. Because spacecraft computational hardware is limited, reducing the computational complexity of navigational algorithms is both desirable and often necessary. By the introduction of a linear system approach to the deputy spacecraft motion, this research avoids the computational burden of integrating the deputy relative equations of motion. In this research, the relative CR3BP equations of motion are derived and linearized using a matrix exponential approximation. This research continues the development of the matrix exponential linearized relative circular restricted three-body problem (CR3BP) equations by applying the dynamics model to estimation and control applications. A simulation is performed to compare state estimation results obtained from using the linearized equations of motion utilizing a Kalman filter and for state estimation utilizing an unscented Kalman filter with the full nonlinear equations of motion. The linearized exponential model is shown to be sufficient for state estimation in the presence of noisy measurements for an example scenario. Additionally, a linear quadratic regulator (LQR) controller was added to optimally control a deputy spacecraft to rendezvous with a chief spacecraft in cislunar space. The contribution of this work is twofold: to provide a proof of concept that the matrix exponential solution for the linearized relative CR3BP equations can be used as the dynamics model for state estimation, as well as to simulate an optimal rendezvous maneuver in the presence of measurement noise.

Bayesian optimization of multi-layer perceptron models for power distribution system state estimation

Abstract Feedforward multilayer perceptron models (MLPs) have been applied to power distribution system state estimation (DSSE) in the past. Existing methods usually employ an ad-hoc or trial and error approach to MLP hyperparameter selection, and thus a systematic way of selecting the optimal hyperparameters including the number of neurons per hidden layer, learning rate, number of training epochs and training batch size is desirable and needed. This paper presents an approach based on Bayesian Optimization with Gaussian Processes for selecting MLP model hyperparameters for state estimation purposes. Results of the optimized MLP models are presented alongside the unoptimized models to compare performance of training, testing, and validation in terms of root-mean-squared-error (RMSE). Additionally, machine learning pipelines were employed and total execution time (seconds) for each trial is presented. The study shows that the MLP models obtained through the proposed optimization method outperform unoptimized models in terms of generalization capability for unseen, new cases.

A data fusion approach for ride-sourcing demand estimation: A discrete choice model with sampling and endogeneity corrections

Ride-sourcing services offered by companies like Uber and Didi have grown rapidly in the last decade. Understanding the demand for these services is essential for planning and managing modern transportation systems. Existing studies develop statistical models for ride-sourcing demand estimation at an aggregate level due to limited data availability. These models lack foundations in microeconomic theory, ignore competition of ride-sourcing with other travel modes, and cannot be seamlessly integrated into existing individual-level (disaggregate) activity-based models to evaluate system-level impacts of ride-sourcing services. In this paper, we present and apply an approach for estimating ride-sourcing demand at a disaggregate level using discrete choice models and multiple data sources. We first construct a sample of trip-based mode choices in Chicago, USA by enriching household travel survey with publicly available ride-sourcing and taxi trip records. We then formulate a multivariate extreme value-based discrete choice model with sampling and endogeneity corrections to account for the construction of the estimation sample from multiple data sources and endogeneity biases arising from supply-side constraints and surge pricing mechanisms in ride-sourcing systems. Our analysis of the constructed dataset reveals insights into the influence of various socio-economic, land use and built environment features on ride-sourcing demand. We also derive elasticities of ride-sourcing demand relative to travel cost and time. Finally, we illustrate how the developed model can be employed to quantify the welfare implications of ride-sourcing policies and regulations such as terminating certain types of services and introducing ride-sourcing taxes.

Intelligent Terahertz Medium Access Control (MAC) for Highly Dynamic Airborne Networks

The Terahertz (THz) link provides over 100 Gbps of data transmission rate. However, it is extremely vulnerable to obstacles and requires very narrow (highly directional) beams for highly focused energy radiation. The main task of THz medium access control (MAC) protocol is not channel access collision avoidance; instead, it is to coordinate all the one-hop neighbors for scheduled, antenna-aligned line-of-sight communications. This study aims to build an intelligent MAC scheme for highly mobile Terahertz airborne networks (TANs). Our TAN MAC design has three features as follows: (1) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Spatio-temporal TAN state learning:</i> We will build a predictive network state estimation model through deep learning and generative adversarial network (GAN). Based on the predicted node/link status, all the one-hop neighbors can prepare well for the antenna alignment and THz channel scheduling. (2) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Accurate, two-level MAC operation control:</i> we propose to use nested deep reinforcement learning (DRL) with outer/inner policy loops for high-/low-level action determination: The outer loop determines the high-level, coarse actions (such as antenna codebook selection); the inner loop determines the low-level, fine actions (such as individual beam control) under the selected high-level action. (3) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TAN-specific, comprehensive MAC behavior control:</i> Based on the above deep neighbor adaptation (DNA) model, we design a complete TAN MAC protocol that considers the routing context and dynamic network topology. Our simulations demonstrate the smooth, high-rate THz communications in MAC layer with resilient RF links under high node mobility.