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During the design of the carbon capture and hydrogen storage process, a static CO 2 hydrogenation poly-generation process for methanol and formic acid synthesis was developed, thereby reducing recycling flow and feedstock losses in the single methanol synthesis process and increasing product diversity. Additionally, a novel flexible poly-generation process was proposed as the product prices fluctuated with the market environment throughout the plant's life cycle. For the static poly-generation process, a mixed integer nonlinear programming model was established, followed by the Mesh Adaptive Direct Search algorithm to determine the optimal parameter design. For flexible processes, with the unscented transform tool, a two-stage optimization strategy is proposed for obtaining optimal design and operating parameters that are computationally tractable. With an input CO 2 flow rate of 200 kmol/h, the results show (1) the static process with an optimal Return On Investment of 13.05%, a fixed investment cost of $ 34.14 × 106, and an annual net profit of 4.46 × 106 $/year; and (2) the flexible process, in which the flexibility indices of the methanol and formic acid production units are 1.088 and 1.194, respectively, resulting the fixed investment cost increase to $ 39.19 × 106, an increase of 14.79%. Accordingly, the annual net profit increased by 8.97% to 4.86 × 106 $/year, while the Return On Investment decreased by 4.67%–12.44%. For investment decision-makers, the flexible poly-generation process can achieve higher profits and reduce potential market risks when they have adequate financing. • A static CO 2 hydrogenation process for MeOH and FA poly-generation process is established. • A MINLP model is developed to obtain the optimal parameter design for the static process. • Conversion continuous variables into scenario variables by unscented transform tool. • The optimal flexible poly-generation process design is obtained by the two-stage optimization strategy. [ABSTRACT FROM AUTHOR]

The development of new reactor technologies requires careful assessments of the various sources of epistemic uncertainties. In this work, nuclear data uncertainties featuring the main isotopes of the U/Th molten salt fast reactor (MSFR) design are propagated through Monte Carlo calculations to quantify the final uncertainty on some relevant integral parameters. In the first part of this paper, some best-estimate calculations are performed by selecting different nuclear data libraries, showing the remarkable impact of this choice on the final responses. Then the Generalized Perturbation Theory routine available in Serpent 2 is adopted for a preliminary sensitivity and uncertainty analyses with respect to keff, highlighting a significant discrepancy between the covariance of the JEFF-3.3 and ENDF/B-VIII.0 libraries. After the selection of a few relevant nuclides, namely, 7Li, 19F, 232Th, and 233U, the Total Monte Carlo method and the unscented transform (UT) are then adopted to estimate the uncertainty of other responses of interest like the conversion ratio and some multigroup constants. Some potential issues of the UT are highlighted, and a mitigation strategy is applied. A relevant result of this analysis concerns the need for better data evaluations for the nuclides constituting the circulating salt for an effective deployment of the MSFR technology. [ABSTRACT FROM AUTHOR]

It is widely recognized that the safe and robust design of a nuclear system requires an uncertainty propagation analysis concerning the various nuclear data used as input parameters, especially in view of the most recent design methodologies like the best-estimate plus uncertainty approach. The evaluation of the input uncertainties and their propagation to the design parameters of interest is particularly important in the case of nuclear fusion machines, such as the Affordable Robust Compact (ARC) reactor, which features the presence of uncommon isotopes in the nuclear engineering field, like fluorine, beryllium, and lithium. The uncertainties of the nuclear data of these nuclides can have a significant impact on the fundamental design parameters, such as the target tritium breeding ratio (TBR). Hence, in this work we investigate the application of different methods for propagating the nuclear data uncertainty to the parameters of interest, computed with the Serpent 2 Monte Carlo code. All the methods proposed in this work share the feature of being nonintrusive, implying that they can be profitably employed independently of the physical and/or computational model adopted. The methods discussed in this work are the fast Total Monte Carlo, the GRS, the unscented transform, and the polynomial chaos expansion. The first three methods led to similar values in terms of relative standard deviation of the TBR due to nuclear data and can be considered as fast alternatives to brute-force sampling methods. For these three methods, the present paper suggests how to select the best approach according to the kind of analysis to be performed and the nuclides considered in the study. The effect of the use of different nuclear data libraries and of different input covariance matrices is also examined. The main outcome of these analyses suggests that the uncertainties in the nuclear data of nickel, fluorine, beryllium, and lithium are sufficiently small (i.e., smaller than 1%) to prevent the TBR from assuming values below the design constraints. The overall uncertainty on the TBR of ARC due to the nuclides here considered was evaluated to be ~0.9%. Concerning the polynomial chaos expansion approach, this paper shows that its application is computationally inefficient compared to the other techniques when the input data dimensionality are very large, as for the case of nuclear data. [ABSTRACT FROM AUTHOR]

The lightning performance of transmission lines is a criterion of utmost importance to be evaluated in transmission system reliability. In order to accurately estimate the expected number of shutdowns, it is imperative to account for the stochastic nature of the lightning phenomenon. In this sense, the most widely used technique to estimate these shutdowns is the Monte Carlo method (MCM). Although the MCM is a trustful method, it has a huge drawback in terms of computational burden. Therefore, this paper proposes the use of an alternative statistical technique, the Unscented Transform method (UTM). This method achieves an approximate non-linear mapping by employing a set of points (associated with input statistical data), which are deterministically selected and weighted. The set of these points and weights is then used to estimate the lightning overvoltage across insulator strings, which represents a significant reduction in the number of samples evaluated in the process in relation to the application from MCM. In this paper, the MCM and UTM methods are applied and compared in the lightning overvoltage evaluation, which is carried out with the help of the Alternative Transient Program (ATP). From the studies conducted, it was found that the application of UTM in place of MCM allowed for significant computational gains while maintaining satisfactory levels of accuracy in the estimation of indicators. • Unscented Transform applied to transmission line lightning performance estimation. • Monte Carlo applied to transmission line lightning performance estimation. • Accelerating the assessment process of transmission line lightning performance. • Monte Carlo and Unscented Transform performance in lightning-related study. [ABSTRACT FROM AUTHOR]

Summary: Finite element (FE) model updating is important and challenging. To address the issues of ill‐conditioning and nonuniqueness, stochastic approaches have been developed for calibrating model parameters and associated uncertainties. Markov chain Monte Carlo (MCMC) methods have been widely used for stochastic model updating by providing a straightforward way to infer the posterior probability density function (PDF) using a sequence of random samples. However, the inherent nature relying on a large number of sampling points to approximate the posterior PDF of model parameters limits their application. In this paper, a Bayesian approach with unscented transform is investigated to perform stochastic model updating in a computational friendly way. Rather than using a large number of randomly chosen sampling points, the proposed approach selects a minimal set of sampling points to represent the PDF of model parameters. On the basis of the unscented transform, this approach effectively explores the distribution of measurements, from which model parameters and associated uncertainties are updated. Numerical simulation of a reinforced concrete beam is presented to show that the proposed approach can achieve similar model updating performance as the MCMC methods. The proposed Bayesian approach is further applied to update the FE model of a real‐world cable‐stayed bridge and provide quantitative assessment of the predication accuracy. [ABSTRACT FROM AUTHOR]

In recent years, the occurrence of cascading failures and blackouts arising from cyber intrusions in the underlying configuration of power systems has increasingly highlighted the need for effective power management that is able to handle this issue properly. Moreover, the growing use of renewable energy resources demonstrates their irrefutable comparative usefulness in various areas of the grid, especially during cascading failures. This paper aims to first identify and eventually protect the vulnerable areas of these systems by developing a hybrid structure-based microgrid against malicious cyber-attacks. First, a well-set model of system vulnerability indices is presented to indicate the generation unit to which the lines or buses are directly related. Indeed, we want to understand what percentage of the grid equipment, such as the lines, buses, and generators, are vulnerable to the outage of lines or generators arising from cyber-attacks. This can help us make timely decisions to deal with the reduction of the vulnerability indices in the best way possible. The fact is that employing sundry renewable resources in efficient areas of the grid can remarkably improve system vulnerability mitigation effectiveness. In this regard, this paper proposes an outstanding hybrid-energy framework of AC/DC microgrids made up of photovoltaic units, wind turbine units, tidal turbine units, and hydrogen-based fuel cell resources, all of which are in grid-connect mode via the main grid, with the aim to reduce the percentage of the system that is vulnerable. To clearly demonstrate the proposed solution's effectiveness and ease of use in the framework, a cyber-attack of the false data injection (FDI) type is modeled and developed on the studied system to corrupt information (for instance, via settings on protective devices), leading to cascading failures or large-scale blackouts. Another key factor that can have a profound impact on the unerring vulnerability analysis concerns the uncertainty parameters that are modeled by the unscented transform (UT) in this study. From the results, it can be inferred that vulnerability percentage mitigation can be achieved by the proposed hybrid energy framework based on its effectiveness in the system against the modeled cyber-attacks. [ABSTRACT FROM AUTHOR]

This paper proposes a new strategy for modeling predictability uncertainty in a stochastic context for decision making within a Virtual Power Plant (VPP). Modeling variable renewable energy generation is an essential step for effective VPP planning and operation. However, it is also a challenging task due to the uncertain nature of its sources. Therefore, developing tools to effectively predict these uncertainties is essential for the optimal participation of VPPs in the electricity market. The purpose of this paper is to present a novel method to model the uncertainties associated with energy dispatching in a VPP using the Unscented Transform (UT) method. The proposed algorithm minimizes the risks associated with the VPP operation in a computationally efficient and simple manner, and can be used in real-time on a power system. The proposed framework was evaluated based on an Electric Power System (EPS) model with historical data. Case studies have been performed to demonstrate the effectiveness of the proposed framework in minimizing power demand and renewable-energy-forecasting uncertainty for a VPP. [ABSTRACT FROM AUTHOR]

The accurate state of charge (SoC) online estimation for lithium-ion batteries is a primary concern for predicting the remaining range in electric vehicles. The Sigma points Kalman Filter is an emerging SoC filtering technology. Firstly, the charge and discharge tests of the battery were carried out using the interval static method to obtain the accurate calibration of the SoC-OCV (open circuit voltage) relationship curve. Secondly, the recursive least squares method (RLS) was combined with the dynamic stress test (DST) to identify the parameters of the second-order equivalent circuit model (ECM) and establish a non-linear state-space model of the lithium-ion battery. Thirdly, based on proportional correction sampling and symmetric sampling Sigma points, an SoC estimation method combining unscented transformation and Stirling interpolation center difference was designed. Finally, a semi-physical simulation platform was built. The Federal Urban Driving Schedule and US06 Highway Driving Schedule operating conditions were used to verify the effectiveness of the proposed estimation method in the presence of initial SoC errors and compare with the EKF (extended Kalman filter), UKF (unscented Kalman filter) and CDKF (central difference Kalman filter) algorithms. The results showed that the new algorithm could ensure an SoC error within 2% under the two working conditions and quickly converge to the reference value when the initial SoC value was inaccurate, effectively improving the initial error correction ability. [ABSTRACT FROM AUTHOR]

To comply with electric power grid automation strategies, new cyber-security protocols and protection are required. What we now experience is a new type of protection against new disturbances namely cyber-attacks. In the same vein, the impact of disturbances arising from faults or cyber-attacks should be surveyed by network vulnerability criteria alone. It is clear that the diagnosis of vulnerable points protects the power grid against disturbances that would inhibit outages such as blackouts. So, the first step is determining the network vulnerable points, and then proposing a support method to deal with these outages. This research proposes a comprehensive approach to deal with outages by determining network vulnerable points due to physical faults and cyber-attacks. The first point, the network vulnerable points against network faults are covered by microgrids. As the second one, a new cyber-security protocol named multi-layer security is proposed in order to prevent targeted cyber-attacks. The first layer is a cyber-security-based blockchain method that plays a general role. The second layer is a cyber-security-based reinforcement-learning method, which supports the vulnerable points by monitoring data. On the other hand, the trend of solving problems becomes routine when no ambiguity arises in different sections of the smart grid, while it is far from a big network's realities. Hence, the impact of uncertainty parameters on the proposed framework needs to be considered. Accordingly, the unscented transform method is modeled in this research. The simulation results illustrate that applying such a comprehensive approach can greatly pull down the probability of blackouts. [ABSTRACT FROM AUTHOR]

This paper proposes an enhanced cyber secure energy and data transaction framework for the optimal operation and management of the smart city. Recently, the concept of smart city within the power systems has taken the center stage. Although the power system full monitoring and accessibility is guaranteed through the smart city concept, the risk of cyber-attacks to the system has been promoted, severely. With this in mind, in this paper an effective smart city model including the smart grid, smart transportation systems (STSs) that consist of the metro and electric vehicles (EVs), microgrid and smart energy hub (EH) is presented. In the proposed model, an improved directed acyclic graph (DAG) approach is represented in order to enhance the security of data transaction within the smart city. In this approach, a security layer is added to the blockchain which will prevent the cyber hackers access to the system information. An effective energy management schedule is also developed in this paper. To do so, an intelligent priority selection (IPS) based on advanced math operators is provided to allocate the metro-owned charging stations (MCSs), optimally. Furthermore, the unscented transform (UT) is utilized to handle the uncertainty of the system parameters. The results showed that the proposed IPS could improve the method CPU time over 75% compared to other well-known meta-heuristic methods in the area. Moreover, the results showed that the proposed framework has remarkably reduced the run time and increased the accuracy of the solution compared to the other meta-heuristic algorithms.

The ongoing development of technology has changed the nature of electric networks in the field of electric power generation. In the same vein, the generation units and demands are getting close by the integration of distributed resources within the distribution system with the aim of bringing more advantages to the system. Considering the promising concept of hybrid networks, which comprises different distributed resources, as a supportive element for the power system, this paper mainly focuses on modeling a distributed hybrid network, through which both the AC and DC loads can be supplied within the hybrid network. This network is operated in a distributed way where no independent operator is assumed for the operation. The primal-dual method of multipliers (PDMM) as an effective distributed method handles the operation of this network. So far, this method has only been applied to the AC grid or DC grid separately, while the presented method is modified for the operation of the hybrid network. Convergence speed and preciseness are mentioned as the advantages of this method which dominates the alternating direction method of multipliers (ADMM). In real cases, there are some errors in the output power of the renewable resources. Aiming to make the presented PDMM method more applicable, the uncertainty is also modelled using the Unscented Transform (UT) approach, as a way of uncertainty modelling which has shown attractive features, especially the capability of correlation modelling; therefore, it is more preferable for uncertainty modeling in many cases compared to the other methods. The proposed work is implemented on a smart island and the authenticity of this work is proved by comparing the performance of the proposed distributed approach with the centralized method.

Automotive embedded systems comprise several domains, such as in software, electrical, electronics, and control. When designing and testing functions at the top level, one generally ignores the uncertainties arising from the electrical and electronic effects, which could lead to an irregular behavior and deteriorate their performance even using the appropriate methodology for designing the embedded control systems. Then, the studies and comparison on the effect of uncertainty in the automotive domain are important to improve the overall performance of those control systems. Here, we explored the uncertainty in control systems using the Monte Carlo (MC) and unscented transform (UT) methods. These methods have been applied to a mobile seat platform (MSP) and a light emitting diode (LED) used for lighting of heavy-duty vehicles. The UT for embedded control systems has shown better performance when compared to the Monte Carlo method, in order to reduce the number of required variables and computational resources in the simulation of failures and test-case generation. Finally, this investigation brings another application for the UT, in order to exemplify its applicability and advantages when compared with the other methods.

In this study, the precise orbit determination (POD) software is developed for optical observation. To improve the performance of the estimation algorithm, a nonlinear batch filter, based on the unscented transform (UT) that overcomes the disadvantages of the least-squares (LS) batch filter, is utilized. The LS and UT batch filter algorithms are verified through numerical simulation analysis using artificial optical measurements. We use the real optical observation data of a low Earth orbit (LEO) satellite, Cryosat-2, observed from optical wide-field patrol network (OWL-Net), to verify the performance of the POD software developed. The effects of light travel time, annual aberration, and diurnal aberration are considered as error models to correct OWL-Net data. As a result of POD, measurement residual and estimated state vector of the LS batch filter converge to the local minimum when the initial orbit error is large or the initial covariance matrix is smaller than the initial error level. However, UT batch filter converges to the global minimum, irrespective of the initial orbit error and the initial covariance matrix.

Abstract A novel real‐time state monitoring method is proposed to realize the real‐time energy management of the lithium‐ion battery packs, which is conducted in the iterative computational calculation process by introducing an improved weighting factor‐unscented Kalman filtering algorithm. The accurate state monitoring treatment is investigated by applying a new iterate calculation thought, in which the improved weight coefficient parameter is constructed and its numerical stability is improved. Meanwhile, the recursive calculation is derived by using the real‐time measured factors, according to which the state‐of‐charge estimation is realized accurately. Aiming to adapt the complex current variation working conditions, the nonlinear treatment is introduced to construct the mathematical unscented transforming function. As can be known from the experimental results, the state‐of‐charge estimation accuracy is 98.34% under the complex current charge‐discharge working conditions. Meanwhile, the effective closed‐circuit voltage trackage is also investigated accurately and its tracking error is within 3.51% in the complex working conditions, which provides a good security guarantee for the reliable energy supply of the lithium‐ion battery packs.

Structural reliability analysis is concerned with estimation of the probability of a critical event taking place, described by P (g (X) ≤ 0) for some n-dimensional random variable X and some real-valued function g. In many applications the function g is practically unknown, as function evaluation involves time consuming numerical simulation or some other form of experiment that is expensive to perform. The problem we address in this paper is how to optimally design experiments, in a Bayesian decision theoretic fashion, when the goal is to estimate the probability P (g (X) ≤ 0) using a minimal amount of resources. As opposed to existing methods that have been proposed for this purpose, we consider a general structural reliability model given in hierarchical form. We therefore introduce a general formulation of the experimental design problem, where we distinguish between the uncertainty related to the random variable X and any additional epistemic uncertainty that we want to reduce through experimentation. The effectiveness of a design strategy is evaluated through a measure of residual uncertainty, and efficient approximation of this quantity is crucial if we want to apply algorithms that search for an optimal strategy. The method we propose is based on importance sampling combined with the unscented transform for epistemic uncertainty propagation. We implement this for the myopic (one-step look ahead) alternative, and demonstrate the effectiveness through a series of numerical experiments. [ABSTRACT FROM AUTHOR]

In recent years, the occurrence of cascading failures and blackouts arising from cyber intrusions in the underlying configuration of power systems has increasingly highlighted the need for effective power management that is able to handle this issue properly. Moreover, the growing use of renewable energy resources demonstrates their irrefutable comparative usefulness in various areas of the grid, especially during cascading failures. This paper aims to first identify and eventually protect the vulnerable areas of these systems by developing a hybrid structure-based microgrid against malicious cyber-attacks. First, a well-set model of system vulnerability indices is presented to indicate the generation unit to which the lines or buses are directly related. Indeed, we want to understand what percentage of the grid equipment, such as the lines, buses, and generators, are vulnerable to the outage of lines or generators arising from cyber-attacks. This can help us make timely decisions to deal with the reduction of the vulnerability indices in the best way possible. The fact is that employing sundry renewable resources in efficient areas of the grid can remarkably improve system vulnerability mitigation effectiveness. In this regard, this paper proposes an outstanding hybrid-energy framework of AC/DC microgrids made up of photovoltaic units, wind turbine units, tidal turbine units, and hydrogen-based fuel cell resources, all of which are in grid-connect mode via the main grid, with the aim to reduce the percentage of the system that is vulnerable. To clearly demonstrate the proposed solution’s effectiveness and ease of use in the framework, a cyber-attack of the false data injection (FDI) type is modeled and developed on the studied system to corrupt information (for instance, via settings on protective devices), leading to cascading failures or large-scale blackouts. Another key factor that can have a profound impact on the unerring vulnerability analysis concerns the uncertainty parameters that are modeled by the unscented transform (UT) in this study. From the results, it can be inferred that vulnerability percentage mitigation can be achieved by the proposed hybrid energy framework based on its effectiveness in the system against the modeled cyber-attacks.

This paper proposes a new strategy for modeling predictability uncertainty in a stochastic context for decision making within a Virtual Power Plant (VPP). Modeling variable renewable energy generation is an essential step for effective VPP planning and operation. However, it is also a challenging task due to the uncertain nature of its sources. Therefore, developing tools to effectively predict these uncertainties is essential for the optimal participation of VPPs in the electricity market. The purpose of this paper is to present a novel method to model the uncertainties associated with energy dispatching in a VPP using the Unscented Transform (UT) method. The proposed algorithm minimizes the risks associated with the VPP operation in a computationally efficient and simple manner, and can be used in real-time on a power system. The proposed framework was evaluated based on an Electric Power System (EPS) model with historical data. Case studies have been performed to demonstrate the effectiveness of the proposed framework in minimizing power demand and renewable-energy-forecasting uncertainty for a VPP.

A robust derivative unscented Kalman filter is proposed for a nonlinear system with non-Gaussian noise and outliers based on Huber function. In this paper, the time update process can be performed using a Kalman filter (KF), and measurement update process can be carried out utilizing an unscented transform. This novel filtering algorithm differs from the traditional unscented Kalman filter (UKF) which is sensitive to non-Gaussian noise and outliers. Besides, for a nonlinear system with linear state equation, the presented filtering has the merits of both KF and UKF, and it has better performance under non-Gaussian noise and large outliers. Furthermore, the proposed filtering algorithm can not only track the target effectively but also suppress the effects of the non-Gaussian noise and large outliers. Finally, numerical simulations are conducted to verify estimation accuracy and effectiveness of the proposed filtering algorithm.

The accurate state of charge (SoC) online estimation for lithium-ion batteries is a primary concern for predicting the remaining range in electric vehicles. The Sigma points Kalman Filter is an emerging SoC filtering technology. Firstly, the charge and discharge tests of the battery were carried out using the interval static method to obtain the accurate calibration of the SoC-OCV (open circuit voltage) relationship curve. Secondly, the recursive least squares method (RLS) was combined with the dynamic stress test (DST) to identify the parameters of the second-order equivalent circuit model (ECM) and establish a non-linear state-space model of the lithium-ion battery. Thirdly, based on proportional correction sampling and symmetric sampling Sigma points, an SoC estimation method combining unscented transformation and Stirling interpolation center difference was designed. Finally, a semi-physical simulation platform was built. The Federal Urban Driving Schedule and US06 Highway Driving Schedule operating conditions were used to verify the effectiveness of the proposed estimation method in the presence of initial SoC errors and compare with the EKF (extended Kalman filter), UKF (unscented Kalman filter) and CDKF (central difference Kalman filter) algorithms. The results showed that the new algorithm could ensure an SoC error within 2% under the two working conditions and quickly converge to the reference value when the initial SoC value was inaccurate, effectively improving the initial error correction ability.

This paper proposes a novel stochastic cascading failure (CF) model with full ac power flow using the unscented transform (UT) for power girds vulnerability analysis. The quasi-steady state (QSS) model is built upon previous dc power flow model by incorporating several enhancements. Our analysis shows that dc power flow does not provide an accurate estimation of the flow process under highly variable generation thus may underestimate the severity of the cascades. The incorporation of full ac power flow constraints allows us to access voltage profiles dynamics during CF in order to simulate voltage-related failures in the grid. The under-voltage load shedding relays are modeled along with a stochastic time-inverse overload relay to accurately simulate the protective system response. In addition, more realistic assumptions are considered in the modeling of wind power penetration using geographical information of grid topology and wind potential map for a given geographical area. The accuracy of the estimated flow process based on UT method is examined under different operating conditions in a 500-bus synthetic network. The proposed model is benchmarked against the historical blackout data and widely used models in the literature showing similar statistical patterns of blackout size. [ABSTRACT FROM AUTHOR]

This article presents a stochastic optimization method for the mechanical dimensioning problem of overhead distribution power lines. For this purpose, an algorithm tailored for solving mixed integer programming problems based on the MI-LXPM algorithm was developed, which, associated with the unscented transform, allows the uncertainty inherent in the wind speed to be considered during the optimization process, with fewer evaluations than required by Monte Carlo methods. An on-the-fly population size adjustment algorithm was used, which allows the required number of evaluations to reach solutions to be reduced further. When compared to a traditional optimization process with deterministic parameters, the results of the proposed method present considerably superior reliability in the case studied, producing a reliable solution in 90.42% of all simulations, while the former produced only 76.39%. [ABSTRACT FROM AUTHOR]

A novel real‐time state monitoring method is proposed to realize the real‐time energy management of the lithium‐ion battery packs, which is conducted in the iterative computational calculation process by introducing an improved weighting factor‐unscented Kalman filtering algorithm. The accurate state monitoring treatment is investigated by applying a new iterate calculation thought, in which the improved weight coefficient parameter is constructed and its numerical stability is improved. Meanwhile, the recursive calculation is derived by using the real‐time measured factors, according to which the state‐of‐charge estimation is realized accurately. Aiming to adapt the complex current variation working conditions, the nonlinear treatment is introduced to construct the mathematical unscented transforming function. As can be known from the experimental results, the state‐of‐charge estimation accuracy is 98.34% under the complex current charge‐discharge working conditions. Meanwhile, the effective closed‐circuit voltage trackage is also investigated accurately and its tracking error is within 3.51% in the complex working conditions, which provides a good security guarantee for the reliable energy supply of the lithium‐ion battery packs. [ABSTRACT FROM AUTHOR]

The changes in the characteristics of the crude oil feed to an atmospheric distillation unit can influence the quality of the products. Also, the crude flow rate disturbances should be handled efficiently. An inferential control structure is adopted using RGA analysis where multiple tray temperatures are controlled. Then, a closed-loop stochastic optimization framework is proposed for tighter control and set of the decision variables is extended using the set-points of the decentralized PI controllers. The closed-loop simulations show improved disturbance rejection performance of the controllers against disturbances of feed flow rate and quality as well. Also, the challenges regarding the specifications of the product quality expressed as equality constraints are addressed. Both single and joint constraints cases are discussed. • Economic optimization of Atmospheric Distillation Units. • Inferential control system is identified using Relative Gain Array analysis. • A robust inferential control system is proposed. • Closed-loop stochastic optimization problem with equality constraint is formulated. • Evaluating dynamic performance of the control system under feed flow disturbances. [ABSTRACT FROM AUTHOR]

In this paper, we propose a method to design Neural Networks with Random Weights in the presence of incomplete data. We present a method, under the general assumption that the data is missing-at-random, to estimate the weights of the output layer as a function of the uncertainty of the missing data estimates. The proposed method uses the Unscented Transform to approximate the expected values and the variances of the training examples after the hidden layer. We model the input data as a Gaussian Mixture Model with parameters estimated via a maximum likelihood approach. The validity of the proposed method is empirically assessed under a range of conditions on simulated and real problems. We conduct numerical experiments to compare the performance of the proposed method to the performance of popular, parametric and non-parametric, imputation methods. By the results observed in the experiments, we conclude that our proposed method consistently outperforms its counterparts. [ABSTRACT FROM AUTHOR]

To comply with electric power grid automation strategies, new cyber-security protocols and protection are required. What we now experience is a new type of protection against new disturbances namely cyber-attacks. In the same vein, the impact of disturbances arising from faults or cyber-attacks should be surveyed by network vulnerability criteria alone. It is clear that the diagnosis of vulnerable points protects the power grid against disturbances that would inhibit outages such as blackouts. So, the first step is determining the network vulnerable points, and then proposing a support method to deal with these outages. This research proposes a comprehensive approach to deal with outages by determining network vulnerable points due to physical faults and cyber-attacks. The first point, the network vulnerable points against network faults are covered by microgrids. As the second one, a new cyber-security protocol named multi-layer security is proposed in order to prevent targeted cyber-attacks. The first layer is a cyber-security-based blockchain method that plays a general role. The second layer is a cyber-security-based reinforcement-learning method, which supports the vulnerable points by monitoring data. On the other hand, the trend of solving problems becomes routine when no ambiguity arises in different sections of the smart grid, while it is far from a big network’s realities. Hence, the impact of uncertainty parameters on the proposed framework needs to be considered. Accordingly, the unscented transform method is modeled in this research. The simulation results illustrate that applying such a comprehensive approach can greatly pull down the probability of blackouts.

In this work, we present a novel scheme for nonlinear hyperspherical estimation using the von Mises–Fisher distribution. Deterministic sample sets with an isotropic layout are exploited for the efficient and informative representation of the underlying distribution in a geometrically adaptive manner. The proposed deterministic sampling approach allows manually configurable sample sizes, considerably enhancing the filtering performance under strong nonlinearity. Furthermore, the progressive paradigm is applied to the fusing of measurements of non-identity models in conjunction with the isotropic sample sets. We evaluate the proposed filtering scheme in a nonlinear spherical tracking scenario based on simulations. Numerical results show the evidently superior performance of the proposed scheme over state-of-the-art von Mises–Fisher filters and the particle filter.

This paper proposes a new general recurrent state-space neuro-fuzzy model structure. Three topologies are under assessment, including the state-input recurrent neuro-fuzzy system, the series-parallel recurrent neuro-fuzzy system and the parallel recurrent neuro-fuzzy system. Moreover, the underlying generalised state-space Takagi–Sugeno system is proven to be a universal approximator, and some stability conditions derived for this system. The online training is carried out based on a constrained unscented Kalman filter, where weights, membership functions and consequents are recursively updated. Results from experiments on a benchmark MIMO system demonstrate the applicability and flexibility of the proposed system identification approach. [ABSTRACT FROM AUTHOR]

The problem of nonaffine time-varying nonlinear control systems is addressed in this paper through an adaptive state-space neuro–fuzzy control scheme. It combines an eight-layered neuro–fuzzy model to approximate nonaffine nonlinear systems’ dynamics with a state feedback quadratic stabilizing controller. Both the neuro–fuzzy model and controller are updated online within a constrained unscented Kalman filter framework. The proposed generalized state-space neuro–fuzzy model is shown to be an universal approximator, and stability conditions derived for time-varying closed-loop systems. Results from a benchmark multi-input and multi-output system demonstrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Highlights • This paper contributes with two new algorithms within the multiple particle filtering framework that provide an accurate and efficient tool to tackle the challenging problem of filtering in high dimensional state spaces. The proposed algorithms significantly outperform the state-of-the-art in particle filtering for high-dimensional state spaces with low computational burden. • The proposed methods make use of a novel combination of sigma-point integration methods and multiple particle filtering to provide a second order approximation to the marginalization integrals required in the multiple filtering field. • Our approach is not restricted for its use with a specific measurement model and does not require analytic derivations. In addition, the computational cost of the presented algorithms is not increased with respect to similar methods. The resulting particle filters outperform the state-ofthe-art particle filters in high-dimensional problems. Abstract In this paper, we introduce two new particle filtering algorithms for high-dimensional state spaces in the multiple particle filtering approach. In multiple particle filtering, the state space is partitioned and a different particle filter is used for each component of the partition. At each time step, all particle filters share information about their marginal densities so that they can adequately approximate the filtering recursion. In this paper, we propose a second order approximation to the involved densities based on sigma-point integration methods. We then introduce two different particle filters that make use of this strategy. Finally, we demonstrate their remarkable performance through simulations of a multiple target tracking scenario with a sensor network. [ABSTRACT FROM AUTHOR]

We present the results of the analysis and comparison of the properties of two concepts in state filtering problems for nonlinear stochastic dynamic observation systems with discrete time: sigma-point Kalman filter based on a discrete approximation of continuous distributions and conditionally minimax nonlinear filter that implements the conditionally optimal filtering method based on simulation modeling. A brief discussion of the structure and properties of the estimates and justifications of the corresponding algorithms is accompanied by a significant number of model examples illustrating both positive applications and limitations of the efficiency for the estimation procedures. The simplicity and clarity of the considered examples (scalar autonomous regressions in the state equation and linear observations) allow us to objectively characterize the considered estimation methods. We propose a new modification of the nonlinear filter that combines the ideas of both considered approaches. [ABSTRACT FROM AUTHOR]

This letter is concerned with an asynchronous localization issue for underwater acoustic sensor networks (UASNs), subject to asynchronous clocks and stratification effects in physical channels. A novel unscented transform-based localization algorithm is proposed to estimate the positions of sensor nodes. Instead of linearizing the measurement equations, the proposed algorithm employs the unscented transform to compute the Jacobian matrix to reduce the linearization errors. Particularly, the ray-tracing approach is adopted to model the stratification effect. Moreover, the convergence analysis and Cramér–Rao lower bound for the algorithm are also provided. Simulation results show that the proposed algorithm can effectively improve the estimation accuracy as compared with the existing works. [ABSTRACT FROM AUTHOR]

In situ visualization of laparoscopic ultrasound in both conventional and robot-assisted laparoscopic surgery requires robust and efficient computation of the pose of the laparoscopic ultrasound probe with respect to the laparoscopic camera. Image-based intrinsic methods of computing this relative pose need to overcome challenges due to irregular illumination, partial feature occlusion, and clutter that are unavoidable in practical laparoscopic surgery. In this paper, we propose an accurate image-based method that is robust to partial occlusion of the fiducials and outliers. The method is extended to multi-view imaging model with applications in stereoscopic laparoscopy and robot-assisted surgery. Rather than treating the model-to-image correspondence and pose computation as separate problems, we solve them jointly using the Kalman Filter-based framework that demonstrates video rate running time (~24fps). By keeping the optical tracking measurements as a reference, we demonstrate that the proposed methods result in clinically acceptable tracking accuracy, reaching target registration errors well below 1.5mm on average. In addition, our multi-view tracking method is compared to a conventional stereo triangulation-based pose estimation scheme that commercial optical tracking systems are based on, to experimentally demonstrate its superiority in terms of accuracy. Finally, we qualitatively demonstrate the suitability of our methods for practical laparoscopic applications by conducting a phantom-based experiment. [ABSTRACT FROM AUTHOR]

This paper investigates the nonlinear system analysis problem for achieving higher computational efficiency and accuracy. A novel and efficient technique, covariance analysis unscented transform describing function technique (CAUTDET), is proposed. This method combines linear system covariance analysis algorithm with describing function method based on unscented transform. Furthermore, the quasi-linearization of the nonlinear system are derived to approximate the nonlinearity, while there is no requirement of the probability density function of the nonlinear system input and needs only one time calculation. Moreover, the proposed technique can be viewed as a generalization method of the nonlinear system design and analysis. Finally, simulations are presented to illustrate the effectiveness of the presented analysis method. [ABSTRACT FROM AUTHOR]

The choice of an appropriate thermodynamic model is one of the critical concerns in the chemical process simulation problems. After the selection of the most appropriate thermodynamic model, the uncertainties associated with the parameters of the model should be taken into account and the effects of such uncertainties should be addressed in the chemical plant design. In this work, the assumption of normal probability distributions for thermodynamic model parameters is coupled with unscented transform for efficient uncertainty propagation and obtaining probabilistic characteristics of output variables of the simulation model. The proposed methodology can be used easily when the input-output representation of the system such as process simulators are used as the modeling tool of the chemical processes. The law sampling points required in the unscented transform formulation can considerably reduce the computational burden while an acceptable accuracy compared to Monte Carlo techniques can be obtained. The methodology is illustrated by two case studies. [ABSTRACT FROM AUTHOR]