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This study systematically investigates the small‐strain stiffness of sand‐rubber mixtures, focusing on combined particle disparity—both larger sand with smaller rubber and smaller sand with larger rubber—using the discrete element method. The effectiveness of various state variables in capturing stiffness behavior across different rubber contents and size disparities (SDs) is evaluated. Conventional state variables developed for natural sands, such as void ratio and mechanical void ratio were found to be less effective in describing the small‐strain stiffness characteristics of sand‐rubber mixtures due to distinct properties of rubber. This study then demonstrates that the stiffness contribution of rubber materials could be negligible, emphasizing that particle property disparity is more significant than SD between sand and rubber materials. Therefore, an adapted state variable, considering only active sand particles, shows improved performance for capturing the correlation between small‐strain stiffness with increasing rubber contents, suggesting its potential utility over conventional variables. Additionally, a refined void ratio, including inactive sand particles but excluding rubber, offers a practical alternative for capturing small‐strain stiffness in experimental and engineering practices, aligning with previous experimental observations. These findings underscore the need for developing more effective state variables that accurately reflect the interactions within heterogeneous materials like sand‐rubber mixtures. [ABSTRACT FROM AUTHOR]

The phase rule of Gibbs is one of the basic equations in phase equilibria. Although it has been with us for 150 years, discussions, interpretations and extensions have been published. Here, the following new content is provided: (i). the choice of independent components is discussed, and the component rule is introduced, (ii). independent state variables are divided into compositional and non-compositional ones, (iii). the generalized phase rule is derived replacing number two in the original phase rule by the number of independent non-compositional state variables introduced above, (iv). the degree of freedom is decreased by the number of compositional constraints in special points (azeotrope and congruent melting) of phase diagrams, (v). a rule is derived connecting the maximum number of coexisting phases with the dimensions of the phase diagram, (vi). examples show how to apply the phase rule to unary, binary and ternary phase diagrams and their sections, (vii). the same is extended with the discussion of calculable and not calculable phase fractions, (viii). it is shown that the current definition of the degree of freedom is not sufficient in the number of cases, (ix). the current definition of the degree of freedom is extended, (x). the application of the generalized phase rule is demonstrated when other non-compositional state variables are applied for nano-phase diagrams, and/or for phase diagrams under the influence of electric potential difference, external magnetic field, mechanical strain or the gravitational field. [ABSTRACT FROM AUTHOR]

Presented in this paper is a direct model predictive control (MPC) for directly controlling the rotor speed and flux of an inverter-fed induction motor, which is also applicable to induction generators. Discrete dynamic model of the induction machine is applied in stationary reference frame with pre-set present and past reference values of the speed and rotor flux for generating the stator current references The uniqueness of the MPC proposed herein is that it does not require outer cascade proportional plus integral (PI) controllers used for regulating rotor speed and flux. Elimination of the outer loop cascade is made possible in this article by the introduction of a unique four-term cost function comprising of weighted magnitudes of the errors between four measured state variables and their reference commands. The four state variables in the unique cost function are the rotor speed, the rotor flux linkage, quadrature axis and direct axis stator currents. The MPC minimizes the cost function at every switching instance by selecting the switching states that gives the least cost function. Consequently, the selected optimal switching states are used to switch optimal inverter output voltages across the machine's stator terminals. As a precursor for obtaining an optimal rotor flux command in the MPC for every torque output, another unique efficiency improvement scheme is developed, which uniquely determines the stator angular velocity and the rotor flux that minimizes the core and copper losses while maintaining a constant slip operation. Therefore, efficacy of the proposed direct model predictive control scheme is verified by comparing its results to results obtained from equivalent vector control on equivalent machine. Results presented show lower loss regime with MPC on optimal stator angular velocity and rotor flux than vector control. [ABSTRACT FROM AUTHOR]

Investigating the chaotic characteristics of classical autonomous systems necessitates prioritizing the dynamic behavior of simple system models, which plays a crucial role in advancing research in this field. This study examines the influence of exponential variations in state variables on the dynamic behavior of chaotic systems, using a classical chaotic system as the main research case. Using classical autonomous systems as the examples, three possible scenarios of exponential change in state variables are listed and the chaotic characteristics of the system under these conditions are demonstrated. The system model exhibits a wide range of chaotic characteristics under different conditions. Additionally, the exponential change of state variables is applied to a simplified chaotic system to illustrate the universality of this phenomenon. Finally, circuit simulation experiments confirm the feasibility of circuit simulations for chaotic system models with exponential change characteristics of state variables, providing theoretical support for the application of such chaotic systems. [ABSTRACT FROM AUTHOR]

Presented in this paper is a direct model predictive control (MPC) for directly controlling the rotor speed and flux of an inverter-fed induction motor, which is also applicable to induction generators. Discrete dynamic model of the induction machine is applied in stationary reference frame with pre-set present and past reference values of the speed and rotor flux for generating the stator current references The uniqueness of the MPC proposed herein is that it does not require outer cascade proportional plus integral (PI) controllers used for regulating rotor speed and flux. Elimination of the outer loop cascade is made possible in this article by the introduction of a unique four-term cost function comprising of weighted magnitudes of the errors between four measured state variables and their reference commands. The four state variables in the unique cost function are the rotor speed, the rotor flux linkage, quadrature axis and direct axis stator currents. The MPC minimizes the cost function at every switching instance by selecting the switching states that gives the least cost function. Consequently, the selected optimal switching states are used to switch optimal inverter output voltages across the machine’s stator terminals. As a precursor for obtaining an optimal rotor flux command in the MPC for every torque output, another unique efficiency improvement scheme is developed, which uniquely determines the stator angular velocity and the rotor flux that minimizes the core and copper losses while maintaining a constant slip operation. Therefore, efficacy of the proposed direct model predictive control scheme is verified by comparing its results to results obtained from equivalent vector control on equivalent machine. Results presented show lower loss regime with MPC on optimal stator angular velocity and rotor flux than vector control.

Using a convective scale WRF-GSI system and a reflectivity observation operator based on the double-moment microphysics (Thompson) scheme, simulated radar reflectivity data are produced and then directly assimilated with EnKF through Observing System Simulation Experiments (OSSEs) for the case of typhoon In-Fa (2021). We examined the ability of the EnKF to simultaneously estimate state variables and conducted sensitivity tests to evaluate the impact of updating different state variables. The results show that updating a full set of analysis variables can help obtain highly precise initial fields in the model and improve typhoon forecast skills. Excluding the horizontal wind update will affect the adjustment of the temperature field and the sea level pressure field during the cyclic assimilation process. Updating the variables directly related to the reflectivity operator alone could adjust hydrometers well, but the positive impact arising from the assimilation quickly vanishes during the forecast. In addition, this study also includes a quantitative RMSE analysis for each variable during the assimilation cycle and compares the effect of each schemes on different variables.

Abstract A procedure for writing the state space equations for linear time invariant circuits is proposed. With this method the state equations for both degenerate and nondegenerate circuits can be derived in a systematic and unified manner. Capacitors' voltages and inductors' currents are chosen as state variables. First all the capacitors and inductors are replaced with voltage and current sources, respectively, and the modified nodal analysis is applied to this new circuit. By inspecting the system of equations obtained in this way and identifying the dependent equations among them, it is possible to detect any degeneracy, that is, dependency among the capacitors' voltages and inductors' currents, and subsequently refine the list of state variables. No knowledge of the circuits' graph is required and only its netlist description is needed in order to derive the equations, which makes it suitable for implementation as a computer program. Several examples are presented in which application of the method is demonstrated.

Copyright of Agronomía Colombiana is the property of Universidad Nacional de Colombia and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

To be able to better develop local folk music and traditional culture, this paper proposes the optimization of local folk music teaching and transmission paths. Analyze the musical styles taught in colleges and universities, learn that ethnic music with a strong national culture is not asked for, build a more comprehensive and three-dimensional system of musical knowledge, master the cultural connotations of ethnic music, strengthen the understanding of their own group culture, and lay the foundation for survival and development in a multicultural society. According to single or multiple continuous or discrete analysis of folk music teaching transmission paths, the state variables of the path optimization process are set to ensure that the growth rate coefficient of the teaching transmission path is positive and the growth rate of teaching is positive. Combining the amount of teaching demand for local ethnic music, the inflection point of the path curve is substituted into the transmission path to obtain the path state variable values. Using the fruit fly optimization algorithm to strengthen the Logistic model, the individual flight distance and direction of fruit flies are preset, and the distance value between the fruit fly position and the origin is calculated to locate toward the target position with visual advantage, and finally the music teaching transmission path optimization is realized. The analysis results show that the logistic model combined with the FOA algorithm has a significantly higher test rate both for subsamples and full samples, and the value of 0.45 is closer to the true value of 0.46, which has a strong applicability and a good path optimization effect.

In this study, we create a novel American double‐barrier Parisian call option contract that may be utilized as an executive option for listed companies to incentivize staff and replace the classic American option. We address the option pricing problem by developing state variables to identify the price state and using the least‐squares Monte Carlo approach. We present several Lévy processes to simulate the movement path of the underlying asset. We discover that geometric Brownian motion and normal inverse Gaussian (NIG) process have successful outcomes, and NIG process has greater calculation accuracy than variance gamma process. The barrier width and window length are positively connected with the price of an American Parisian option, whereas the strike price is negatively correlated with it. Increasing the number of discrete periods of the contract will enhance the pricing accuracy. [ABSTRACT FROM AUTHOR]

[Display omitted] Memristor devices offer an alternative route to transistor scaling and the exponential growth of computational resources. Numerous memristors have the potential to be used in neuromorphic computing or to complement the von Neumann architecture-based digital computation. However, most of the available reports on memristor are based on the first-order devices, which are unable to mimic all the biological properties. In contrast, recent studies have exposed that the conduction mechanism of the memristors is influenced by the multiple internal state variables, making them higher-order memristors, that exhibit complex dynamical behavior for encoding temporal information. In this review paper, we discuss the different types of higher-order memristors, including their mechanism and applications. We also highlight the material properties that enable the higher-order complexity in these devices. The presence of multiple internal state variables in higher-order memristors enable the dynamic complexity and adaptive properties, mimicking the neuronal and synaptic properties, similar to biological system. These higher-order memristors show the potential for processing temporal information and performing analogue computing. Further efforts are needed to optimize higher-order memristor devices and develop new architectures for future bio-realistic neuromorphic computing. [ABSTRACT FROM AUTHOR]

Direct-drive wind farms (DDWF) merged into weak AC systems have the risk of sub-synchronous oscillations (SSO). Because the DDWF model has a higher order, it needs to be reduced for actual analysis. When analyzing the SSO problems, the existing balanced reduction method cannot accurately keep SSO modes of the system during the reduction process. To solve this problem, a balanced reduction method for SSO analysis is proposed. This method is based on the participation factor analysis and retains the state variables which strongly related to the SSO model. Combining with the Hankel singular value to determine the reduction order, a reduced-order system model consistent with the SSO characteristics and dynamic characteristics of the full-order system model is established. Firstly, a balanced system model by balancing the small signal model of the full-order system is established. Then, the participation factor analysis is performed on the balanced system model, combined with the Hankel singular value to ensure the set of retained state variables, and the residual order reduction method is used to establish the reduced-order system model. Finally, by comparing the full-order system model and the reduced-order system model, the results show that the proposed reduced-order method is suitable for the study of DDWF integration into weak AC system SSO.

The purpose of the article is to develop a method and algorithm for the accelerated calculation of steady states of thyristor converters using computer models of converters based on the use of the theory of difference equations in the form of recurrent linear relationships for state variables on the boundaries of the converter periods. Methodology. The article is devoted to the solution of the problem of reducing the cost of computer time to achieve the steady state of the thyristor converter. For this, it is proposed to use difference equations, for which the values of the state variables at the limits of the periods of the converter's operation are taken as variables. These values are accumulated during the initial periods of the transient process of the converter, after which the coefficients of the difference equations are calculated, and the following limit values of the state variables are found using the defined difference equations. A program in the algorithmic language of the MATLAB system is presented, which implements the proposed method and algorithm compatible with the visual model of the converter. Results. The theoretical foundations of the proposed method and the area of its applicability are substantiated. Recommendations are presented for determining the number of periods of the flow process that must be calculated for further implementation of the method. An algorithm for forming matrix relations for determining the coefficients of difference equations with respect to the values of state variables at the boundaries of periods is shown. Matrix equations are given that allow calculating the parameters of the steady state. All stages of the algorithm are illustrated with numerical examples. Originality. The method rationally combines all the advantages of visual modeling based on the numerical integration of equations using the method of state variables for the periods of operation of the converter with the analytical solution of the recurrence relations obtained on this basis for the values of state variables at the boundaries of adjacent periods. Practical value. The proposed method makes it possible to reduce by several orders of magnitude the computer time spent on calculating the parameters of the steady-state mode of the converter and, at the same time, to significantly improve the accuracy of these calculations. The practical application of the method is very effective in research and design of thyristor converters of electrical energy parameters.

Copyright of Electric Power Engineering Technology is the property of Editorial Department of Electric Power Engineering Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The purpose of the article is to develop a method and algorithm for the accelerated calculation of steady states of thyristor converters using computer models of converters based on the use of the theory of difference equations in the form of recurrent linear relationships for state variables on the boundaries of the converter periods. Methodology. The article is devoted to the solution of the problem of reducing the cost of computer time to achieve the steady state of the thyristor converter. For this, it is proposed to use difference equations, for which the values of the state variables at the limits of the periods of the converter's operation are taken as variables. These values are accumulated during the initial periods of the transient process of the converter, after which the coefficients of the difference equations are calculated, and the following limit values of the state variables are found using the defined difference equations. A program in the algorithmic language of the MATLAB system is presented, which implements the proposed method and algorithm compatible with the visual model of the converter. Results. The theoretical foundations of the proposed method and the area of its applicability are substantiated. Recommendations are presented for determining the number of periods of the flow process that must be calculated for further implementation of the method. An algorithm for forming matrix relations for determining the coefficients of difference equations with respect to the values of state variables at the boundaries of periods is shown. Matrix equations are given that allow calculating the parameters of the steady state. All stages of the algorithm are illustrated with numerical examples. Originality. The method rationally combines all the advantages of visual modeling based on the numerical integration of equations using the method of state variables for the periods of operation of the converter with the analytical solution of the recurrence relations obtained on this basis for the values of state variables at the boundaries of adjacent periods. Practical value. The proposed method makes it possible to reduce by several orders of magnitude the computer time spent on calculating the parameters of the steady-state mode of the converter and, at the same time, to significantly improve the accuracy of these calculations. The practical application of the method is very effective in research and design of thyristor converters of electrical energy parameters. [ABSTRACT FROM AUTHOR]

The mechanical behaviour of materials can be described by a phenomenological relationship that binds strain to stress, by the complex modulus function: M (ω) , which represents the frequency response of the medium in which a transverse mechanical wave is propagated. From the experimental measurements of the internal friction obtained when varying the frequency of a transverse mechanical wave, the parameters that characterize the complex module are determined. The internal friction or loss tangent is bound to the dissipation of the specific mechanical energy. The non-equilibrium thermodynamics theory leads to a general description of irreversible phenomena such as relaxation and viscosity that can coexist in a material. Through the state variables introduced by Ciancio and Kluitenberg, and applying the fractional calculation due to a particular memory mechanism, a model of a viscoanelastic medium is obtained in good agreement with the experimental results. [ABSTRACT FROM AUTHOR]

Phasor Measurement Units (PMUs) provide Global Positioning System (GPS) time-stamped synchronized measurements of voltage and current with the phase angle of the system at certain points along with the grid system. Those synchronized data measurements are extracted in the form of amplitude and phase from various locations of the power grid to monitor and control the power system condition. A PMU device is a crucial part of the power equipment in terms of the cost and operative point of view. However, such ongoing development and improvement to PMUs' principal work are essential to the network operators to enhance the grid quality and the operating expenses. This paper introduces a proposed method that led to lowcost and less complex techniques to optimize the performance of PMU using Second-Order Kalman Filter. It is based on the Asyncrhophasor technique resulting in a phase error minimization when receiving the signal from an access point or from the main access point. The MATLAB model has been created to implement the proposed method in the presence of Gaussian and non-Gaussian. The results have shown the proposed method which is Second-Order Kalman Filter outperforms the existing model. The results were tested usingMean Square Error (MSE). The proposed Second-Order Kalman Filter method has been replaced with a synchronization unit into thePMUstructure to clarify the significance of the proposed new PMU. [ABSTRACT FROM AUTHOR]

Increasing energy needs, pollution of nature, and eventual depletion of resources have prompted humanity to obtain new technologies and produce energy using clean sources and renewables. In this paper, we design an advanced method to improve the performance of a sliding mode controller combined with control theory for a photovoltaic system. Specifically, we decouple the controlled output of the system from any perturbation source and assess the effectiveness of the results in terms of solution quality, closed-loop control stability, and dynamical convergence of the state variables. This study focuses on the climatic conditions that may affect the behavior of a solar energy plant to supply a motor with the highest possible efficiency and nominal operating conditions. The designed method enables us to obtain an optimal performance by means of advanced control techniques and a slime mould stochastic optimization algorithm. The efficiency and performance of this method are examined based on a benchmark model of a photovoltaic system via numerical analysis and simulation. [ABSTRACT FROM AUTHOR]

Hydraulic State Estimation allows to estimate the most likely state of a water supply network considering multiple sources of information and their associated uncertainty. It is set out as an optimization problem, often addressed according to the Weighted Least Squares criterion. It can be formulated differently depending on the selected set of state variables. This choice is not straightforward and leads to different problem dimensions, time complexities and convergence behavior. All possible essential approaches are gathered in this work and two are identified as the most suitable according to the resulting problem dimensions: a head-based approach and a demand-based approach. The particularities and the formulation according to both sets of state variables are discussed, seeking for efficient implementation. Their time complexity and convergence behavior are compared to draw conclusions that help to identify the most suitable approach for real practice applications. [ABSTRACT FROM AUTHOR]

The present work aims at analyzing issues related to the data manifold dimensionality. The interest of the study is twofold: (i) first, when too many measurable variables are considered, manifold learning is expected to extract useless variables; (ii) second, and more important, the same technique, manifold learning, could be utilized for identifying the necessity of employing latent extra variables able to recover single-valued outputs. Both aspects are discussed in the modeling of materials and structural systems by using unsupervised manifold learning strategies.

Identifying the state of an ecosystem and any changes in its state can help in detecting critical ecosystem transitions and formulating adaptive measures to reduce losses of ecosystem services. Such efforts are important for regional sustainability. In this study, we propose a new perspective by identifying grassland ecosystem states as patterns of ecosystem service bundles. The total annual provisioning of five key grassland ecosystem services in Xilin Gol, including net primary productivity, soil conservation, soil erosion by wind, water yield, and water retention, was estimated from 2001 to 2014. The ecosystem service bundle in each pixel was represented by the ecosystem services, which accounted for 75% of the total ecosystem services after the values of the five ecosystem services for that pixel were ranked in descending order. Then, we selected the four main ecosystem service bundles that covered 99% of the area of Xilin Gol and analyzed their patterns, characterized by landscape indices, from 2001 to 2014. Finally, the influences of precipitation, temperature, solar radiation, and vegetation coverage on the patterns of ecosystem service bundles were explored through stepwise regression. The results showed that vegetation coverage greatly affected the patterns of ecosystem service bundles and promoted the shape complexity and diversity of the bundle patches. The main factors affecting bundle diversity were precipitation in meadow steppes, vegetation coverage and temperature in typical steppes, and temperature in desert steppes. Moreover, the ranges of the landscape indices mean patch size and area-weighted mean fractal dimension fluctuated greatly in ecosystems with lower stability. The results of this study confirmed the hypothesis that patterns in ecosystem service bundles can reflect ecosystem states and indicated that changes in ecosystem service bundle patterns can be used as early warnings of critical transitions in ecosystems.

We tackle the obtaining of canonical forms for classifying linear control systems with regard to changes in the state variables. Although it was solved in the 1980s for controllable systems, it is still an open problem for the general case of multiparametric non-necessarily controllable systems. Here we obtain a general reduced form which is canonical for a class of systems which includes the cases already known (uniparametric systems, controllable systems). [ABSTRACT FROM AUTHOR]

Engineering resilience, a forest's ability to maintain its properties in the event of disturbance, comprises two components: resistance and recovery. In human‐dominated landscapes, forest resilience depends mostly on recovery. Forest recovery largely depends on autogenic regulation, which entails a negative feedback loop between rates of change in forest state variables and state variables themselves. Hence community dynamics change in response to deviations from forest equilibrium state. Based on the premise that autogenic regulation is a key aspect of the recovery process, here we tested the hypothesis that combined old‐growth forest (OGF) and secondary forest (SF) dynamics should show autogenic regulation in state variables, and thus convergence towards OGF‐based reference points, indicating forest resilience.We integrated dynamic data for OGF (11‐year monitoring) and SF (16‐year monitoring) to analyse three key state variables (basal area, tree density and species richness), their annual rates of change and their underlying demographic processes (recruitment, growth and mortality). We examined autogenic regulation through generalized linear mixed‐effects models (GLMMs) to quantify functional relationships between rates of change of state variables (and underlying demographic processes) and their respective state variables.State variables in OGF decreased moderately over time, against our prediction of OGF constancy. In turn, the three state variables analysed showed negative relationships with their respective rates of change, which allows the return of SF to OGF values after disturbance. In all cases, recruitment decreased with increasing values in state variables, while mortality increased.The observed negative relationships between state variables, their rates of change and their underlying demographic processes support our hypothesis of integrated OGF and SF dynamics showing autogenic regulation for state variables. Competition seems to be a major driver of autogenic regulation given its dependence on a resource availability that declines as forest structure develops.Synthesis. Based on a straightforward and comprehensive approach to quantify the extent to which tropical forest dynamics is self‐regulated, this study highlights the importance of autogenic regulation for tropical dry forest as a basic component of its resilience. This approach is potentially valuable for a generalized assessment of engineering resilience of forests world‐wide. [ABSTRACT FROM AUTHOR]

Most simulation packages provide long line models containing only two input terminals and two output terminals. These models allow testing transient states initiated by the occurrence of interference at the ends of the line. For this reason, it is not possible to study disorders occurring at any point of the electrical network. The article describes in detail the mathematical models of the long line, their implementation in the Matlab environment and exemplary results of computer simulations. Keywords: power line, state variables, differential scheme

The existing literature finds that information not captured by traditional term structure factors helps predict excess bond returns. When estimating no-arbitrage affine term structure models, aligning in-sample and out-of-sample objective functions results in term structure factors that capture information that remains hidden from existing approaches. Specifically, the estimates of the third term structure factor radically differ and are related to the fourth principal component, which helps forecast bond returns. The new objective function leads to substantial improvements in forecasting performance. It also results in higher model term premiums and lower expected future short rates. This paper was accepted by David Simchi-Levi, finance. [ABSTRACT FROM AUTHOR]

Delineating the boundary between troposphere and stratosphere in a chemistry transport model requires a state variable for each air mass that maps out the ever shifting, overlapping three-dimensional (3-D) boundary at each time step. Using an artificial tracer, e90, with surface sources and 90 day decay time, the model e90 tropopause matches the 1-D temperature lapse rate definition of the tropopause as well as the seasonal variation of ozone at this boundary. This approach works from equator to pole, over all seasons, unlike methods based on potential vorticity or ozone. By focusing on the time scales that separate stratosphere from troposphere, we examine the cause of ozone seasonality at the midlatitude tropopause, the oldest air in the troposphere (winter descent in the subtropics), and a north-south bias in the age of air of the lowermost stratosphere as evaluated using a northern tracer. The tracer e90 is invaluable in 3-D modeling, readily separating stratosphere from troposphere and a giving quantitative measure of the effective distance from the tropopause.

Fault detection in hybrid AC–DC distribution networks is a challenging problem due to various sources of uncertainty and high degrees of complexity. A few well-known sources that instil uncertainty in the system are stochasticity of energy injected by distributed energy resources, noisy or corrupt data, heterogeneity of agents, problems with the automated mapping of equipment connectivity, and partial knowledge of the system. This study presents a distinctive approach that draws upon the use of Bayesian belief network to overcome uncertainties. The key advantage of Bayesian inference methodology is its capability to leverage both causal and correlational data in formulating a plausible conclusion. The proposed method uses state variables produced by distributed state estimation along with data collected from self-aware agents as the main sources of causal information. The rationale for using state estimation is its capability to overarch heterogeneity of AC and DC agents. It is shown that probabilistic graphical models can be employed successfully to detect faults in active hybrid distribution networks. An augmented version of IEEE 13-bus network is utilised to simulate and verify the suitability and effectiveness of the proposed technique.

The relevance of this work is conditioned by the need for maintaining a given temperature at the outlet of a heat exchanger under the conditions of variable flow rate and composition of the feedstock. This work reviews the methods for improving the heat exchanger control. The parameters of the control object are selected and its features are determined. A dynamic model of a heater with a steam space (shell-and-tube evaporator) applied for heating the gasoline fraction at the outlet of the topping column of the oil stabilization unit is obtained. The part of the distillate and fractional composition of the gasoline fraction, which is a disturbing factor, are determined. The mathematical model of the shell-and-tube evaporator is presented as an object with lumped parameters in the form of heat balance in a differential form taking into account transport delay. The heat transfer coefficient is determined from the side of gasoline fraction for the bubble boiling mode and from the side of stripped oil for the turbulent mode. A single-loop automated control system (ACS) and an ACS with the participation of the obtained model are implemented within Simulink. In the first case, the object model is represented in the canonical form in the state space. The intermediate signals (from matrix feedback amplifiers) are used as the control actions. In the second case, a combined temperature control system is developed at the outlet of the heat exchanger. The output signal of the model is supplied as a current parameter to controller 2, which generates a control signal that compensates for the disturbance. The task of controller 2 is the output signal of controller 1, which adjusts the operation according to the output temperature of the object. A comparison of the control methods with the use of standard controllers and with the participation of the developed object model is given. It is shown that the indicators of the transition process are higher when applying the models in the control. [ABSTRACT FROM AUTHOR]

This study proposes a state formulation of the space‐vector dynamic model of the Synchronous Reluctance Motor (SynRM) considering both saturation and cross‐saturation effects. The proposed model adopts the stator currents as state variables and has been theoretically developed in both the rotor and stator reference frames. The proposed magnetic model is based on a flux versus current approach and relies on the knowledge of 11 parameters. Starting from the definition of a suitable co‐energy variation function, new flux versus current functions have been initially developed, based on the hyperbolic functions and, consequently, the static and dynamic inductance versus current functions have been deduced. The dynamic inductance functions have been derived so to fulfill the reciprocity conditions. This study presents also a technique for the estimation of the parameters of the proposed magnetic model, which is based on stand‐still tests without the need to lock the rotor. The identification process has been performed based on the minimization of a suitably defined error function including the difference between the measured and estimated stator fluxes. The proposed parameter estimation technique has been tested in both numerical simulation and experimentally on a suitably developed test set‐up, permitting the experimental validation of the proposed model. [ABSTRACT FROM AUTHOR]