Parameters Of Dynamic Model Research Articles

Improvement of Identification Procedure Using Hybrid Cuckoo Search Algorithm for Turbine-Governor and Excitation System

In this paper a new method is introduced in order to modify identification process of a gas power plant using a metaheuristic algorithm named Cuckoo Search (CS). Simulations play a significant role in dynamic analyses of power plants. This paper points out to a practical approach in model selection and parameter estimation of gas power plants. The identification and validation process concentrates on two subsystems: governor-turbine and exciter. Standard models GGOV1 and STB6 are preferred for the dynamical structures of governor-turbine and exciter respectively. Considering definite standard structure, main parameters of dynamical model are pre estimated via system identification methods based on field data. Then obtained parameters are tuned carefully using an iterative Cuckoo algorithm. Models must be validated by results derived via a trial and error series of simulation in comparison to measured test data. The procedure gradually yields in a valid model with precise estimated parameters. Simulation results show accuracy of identified models. Besides, a whiteness analysis has been performed in order to show the authenticity of the proposed method in another way. Despite various detailed models, practical attempts of model selection, identification, and validation in a real gas unit could rarely be found among literature. In this paper, Chabahar power plant in Iran, with total install capacity of 320 MW, is chosen as a benchmark for model validation.

Development and Validation of a New Oscillatory Component Load Model For Real-Time Estimation of Dynamic Load Model Parameters

This paper presents the development and validation of a new oscillatory component load (OCL) model for a measurement-based real-time estimation of dynamic load model parameters. The proposed OCL model has been derived from a second-order differential equation that considers three necessary components, namely static, exponential recovery, and damped oscillatory components. The performance evaluation of the proposed OCL model has been demonstrated through DIgSILENT PowerFactory-based dynamic power system simulations, real-time substation data captured from phasor measurement units located at the primary substations of Leadenham, Holwell, and Regent Street cities, and real-time transient voltage events data provided by the EirGrid. Further, the effectiveness of the proposed OCL model has been assessed by comparing its performance with that of the existing exponential recovery load model. A new flexible and efficient SynchroHub platform, developed at the University of Manchester for real-time data acquisition, processing and visualization, has been used for the real-time estimation of dynamic load model parameters. The validation results revealed the efficacy of the proposed OCL model in tracking both the first as well as the subsequent oscillations of dynamic load model response of the power system for both the steady state as well as transient operating conditions accurately.

Variation and trends in transmission dynamics of Methicillin-resistant Staphylococcus aureus in veterans affairs hospitals and nursing homes

Variation and differences of MRSA transmission within and between healthcare settings are not well understood. This variability is critical for understanding the potential impact of infection control interventions and could aid in the evaluation of future intervention strategies. We fit a Bayesian transmission model to detailed individual-level MRSA surveillance data from over 230 Veterans Affairs (VA) hospitals and nursing homes. Our approach disentangles the effects of potential confounders, including length of stay, admission prevalence, and clearance, estimating dynamic transmission model parameters and temporal trends. The median baseline transmission rate in hospitals was approximately four-fold higher than in nursing homes, and declined in 46% of hospitals and 9% of nursing homes, resulting in a median transmission rate reduction of 43% across hospitals and an increase of 2% in nursing homes. For first admissions into an acute care facility, the median (range) importation probability was 10.5% (5.9%–18.4%), and was nearly twice as large, 18.7% (9.2%–37.4%), in nursing homes. This analysis found differences within and between hospitals and nursing homes. The transmission rate declined substantially in hospitals and remained stable in nursing homes, while admission prevalence was considerably higher in nursing homes than in hospitals.

Indoor robot path planning assisted by wireless network

Indoor robot global path planning needs to complete the motion between the starting point and the target point according to robot position command transmitted by the wireless network. Behavior dynamics and rolling windows in global path planning methods have limitations in their applications because the path may not be optimal, there could be a pseudo attractor or blind search in an environment with a large state space, there could be an environment where offline learning is not applicable to real-time changes, or there could be a need to set the probability of selecting the robot action. To solve these problems, we propose a behavior dynamics and rolling windows approach to a path planning which is based on online reinforcement learning. It applies Q learning to optimize the behavior dynamics model parameters to improve the performance, behavior dynamics guides the learning process of Q learning and improves learning efficiency, and each round of intensive learning action selection knowledge is gradually corrected as the Q table is updated. The learning process is optimized. The simulation results show that this method has achieved remarkable improvement in path planning. And, in the actual experiment, the robot obtains the target location information by wireless network, and plans an optimized and smooth global path online.

Joint parameter identification, vibration and noise analysis of gearbox

A certain type of gearbox is investigated for the problem that the stiffness and damping of bearings are difficult to be accurately determined and then affect the analysis of vibration and noise of gearbox. Firstly, a coupled dynamic lumped parameter model of three-stage helical gear system with consideration of bearing stiffness, bearing damping, and transmission error is established. The modal parameters of gear system are obtained by using the experimental modal analysis method with single-input and multiple-output. The equation for joint parameter identification of gearbox is established which is based on the experimental modal analysis theory and the dynamic lumped parameter model, and subsequently the parameters of the joint are obtained by the least square method. Then, a gear-shaft-bearing- housing coupled dynamic finite element model is developed on the basis of the identified parameters, and after that the dynamic response results of gearbox are solved by using the modal superposition method and compared with the vibration test results. Finally, an acoustic boundary element model of gearbox is established by taking the dynamic response results as the acoustic boundary condition, and the surface sound pressure and radiation noise of gearbox are solved by the boundary element method (BEM), and then the results are compared with the noise test. The results show that the simulation laws and test laws are in good agreement, and thus the method of joint parameter identification, vibration and noise analysis of gearbox is feasible.

Forecasting of recessions via dynamic probit for time series: replication and extension of Kauppi and Saikkonen (2008)

In this work, we first replicate the results of the fully parametric dynamic probit model for forecasting US recessions from Kauppi and Saikkonen (Rev Econ Stat 90(4):777–791, 2008) [which is in the spirit of Estrella and Mishkin (Rev Econ Stat 80(1):45–61, 1998) and Dueker (Rev Fed Reserve Bank St Louis 79(2):41–51, 1997)] and then contrast them to results from a nonparametric local-likelihood dynamic choice model for the same data. We then use expanded data to gain insights on whether these models could have warned the public about approach of the latest recession, associated with the Global Financial Crisis. Finally, we also apply both approaches to gain insights for 2018.

Simultaneous Parameter Optimization of an Arctic Sea Ice–Ocean Model by a Genetic Algorithm

AbstractImprovement and optimization of numerical sea ice models are of great relevance for understanding the role of sea ice in the climate system. They are also a prerequisite for meaningful prediction. To improve the simulated sea ice properties, we develop an objective parameter optimization system for a coupled sea ice–ocean model based on a genetic algorithm. To take the interrelation of dynamic and thermodynamic model parameters into account, the system is set up to optimize 15 model parameters simultaneously. The optimization is minimizing a cost function composed of the model–observation misfit of three sea ice quantities (concentration, drift, and thickness). The system is applied for a domain covering the entire Arctic and northern North Atlantic Ocean with an optimization window of about two decades (1990–2012). It successfully improves the simulated sea ice properties not only during the period of optimization but also in a validation period (2013–16). The similarity of the final values of the cost function and the resulting sea ice fields from a set of 11 independent optimizations suggest that the obtained sea ice fields are close to the best possible achievable by the current model setup, which allows us to identify limitations of the model formulation. The optimized parameters are applied for a simulation with a higher-resolution model to examine a portability of the parameters. The result shows good portability, while at the same time, it shows the importance of the oceanic conditions for the portability.

Plant functional indicators of vegetation response to climate change, past present and future: II. Modal plant functional types as response indicators for present and future climates

An assessment of plant functional indicators of vegetation response to climate change requires a science-based evaluation of functional linkages between plants and climate. This paper addresses the hypotheses outlined in the preceding paper: 1) A whole-plant system of modal plant functional types (PFTs) based on a core set of functional traits can provide an improved alternative to PFTs and traits used so far in models of vegetation response to climate change; 2) Modal PFTs are more efficient indicators of vegetation response to climate change than individual traits; 3) Improved plant functional selection criteria can lead to improved parameters in Earth System and Dynamic Global Vegetation Models. Present-day (1975 centred) plant–climate relationships were analyzed within a global climatic framework of 1327 (200 m2) sample sites covering all major biomes. Five regions were selected for their range of bioclimatic gradients (arctic-boreal; cool temperate-subtropical; humid equatorial; hot, cold deserts). Biophysical site data were recorded along gradient oriented transects (gradsects) using a standard protocol that included all vascular plant taxa and modal PFTs. Regression, ordination and species distribution modeling were used to assess plant–climate indicator values against ten bioclimatic variables including extremes of contemporary thermal, light (energy) and moisture availability and estimates for ‘present’ (1975) and year 2100 climate scenarios. Modal PFTs and individual traits were highly correlated in differing patterns with bioclimate in each of the five regions in both climate scenarios with PFTs being the best predictors. Each of the three hypotheses is supported by these results, indicating that preference should be given to modal assemblages rather than individual traits as response parameters in predictive vegetation models. The indicators identified here are but one element of the highly complex array of other factors that influence vegetation response to terrestrial environmental change such as resource use, species acclimation, invasive species and human population pressure.

FRACTIONAL ORDER PID CONTROLLER FOR AGC LOOP OF A TWO-AREA SOLAR-THERMAL DEREGULATED POWER SYSTEM WITH RFB AND IPFC UNIT

This paper presents the Fractional Order Proportional-Integral-Derivative (FOPID) controller for Automatic Generation Control (AGC) of an interconnected two area solar-thermal deregulated power system. In this investigation, the thermal plants are considered with reheat tandem compound steam turbine rather than steam turbine dynamic model parameters are thought to be consistent. Concentrated solar power can store vitality in the type of thermal energy. The stored thermal energy can be utilized to produce electricity in the absence of solar irradiance. Thus, AGC of a multi-area power system incorporating solar thermal power plant (STPP) is vital for these examinations. A FOPID controller is an established PID aside from its derivative and integral orders are fractional numbers in place of being integers. The control parameters of FOPID controller are tuned utilizing Lightning Search Algorithm (LSA) and its execution is contrasted and Proportional-Integral (PI), Proportional-Integral-Derivative (PID) controllers based AGC loop. A sophisticated application of Redox Flow Batteries (RFB) coordinates with Interline Power Flow Controller (IPFC) for the improvement of AGC loop of a two-area solar-thermal power system is also considered. Simulation reveals that the proposed FOPID controller tuned with LSA algorithm liven up the dynamic output response of the test system as far as less pinnacle deviation and settling time of area frequencies and tie-line power oscillations in various exchanges of the deregulated power system. The execution of the RFB and IPFC unit captures the underlying fall in frequency and additionally the tie-line power deviations after a sudden load unsettling influence.

Analyzing the Electronic Coupling in Molecular Crystals—The Instructive Case of α‐Quinacridone

AbstractIn the present article, an evaluation of different approaches for estimating the electronic coupling and charge‐transport parameters in organic semiconductors is provided. As a testbed for that comparison, the α‐polymorph of quinacridone is chosen. This system is particularly well suited for the purpose, as α‐quinacridone intermolecular interactions in distinct crystallographic directions are dominated by the three mechanisms most relevant in organic semiconductors: π‐stacking, H‐bonding, and van der Waals stacking. Density‐functional theory‐based simulations yield a comparably complex band structure, which provides the means for demonstrating shortcomings of commonly applied approaches. These include the estimation of transport properties based on bandwidths and the calculation of electronic transfer integrals considering molecular dimers. As a particularly promising alternative, the fitting of suitably complex tight‐binding models to the DFT‐calculated bands in the entire Brillouin zone is proposed. These fits bear the advantage of directly producing intermolecular coupling constants for all relevant neighboring molecules as input parameters for hopping and dynamic disorder models. They also yield an analytic expression for the electronic bands. These allow the extraction of parameters relevant for band‐transport models (like group velocities and effective masses) in the entire Brillouin zone.

Varying constants driven baryogenesis

We study the spontaneous baryogenesis scenario in the early universe for three different frameworks of varying constants theories. We replace the constants by dynamical scalar fields playing the role of thermions. We first obtain the results for baryogenesis driven by the varying gravitational constant, G, as in the previous literature, then challenge the problem for varying fine structure constant alpha models as well as for varying speed of light c models. We show that in each of these frameworks the current observational value of the baryon to entropy ratio, eta _B sim 8.6 cdot 10^{-11}, can be obtained for large set of parameters of dynamical constants models as well as the decoupling temperature, and the characteristic cut-off length scale.

Bayesian consideration of unknown sensor characteristics in fatigue-related structural health monitoring

In structural health monitoring, Bayesian updating is widely utilised in the analysis of noisy sequential data of dynamic systems with the objective of determining the state of damage of a structure or identifying its unknown dynamic characteristics or both. In the present work, this approach is enhanced to encompass the simultaneous handling of insufficient knowledge of sensor features – i.e. a non-applicable relation between state of damage and observations due to high uncertainty introduced by unknown measuring parameters – while given the nature of damage propagation as in fatigue-driven applications. Thereby, the statistical inversion problem of inferring unknown states of damage as well as unknown measuring and dynamic model parameters is addressed solely on the basis of observations and parameter-dependent functional expressions linking these quantities. As Bayesian updating provides the posterior belief on the unknown quantities in form of probability density functions, the question of state observability and parameter identifiability can be approached simultaneously. The methodology is applied to potential-drop measuring in a fatigue-loading scenario and its effectiveness is successfully demonstrated.

Response analysis of shale bedding structure to ultrasonic characteristics and its application

Abstract Based on the wave theory, different bedding structure models for shales in Lower Silurian Longmaxi Formation of southeastern Chongqing area were established, numerical simulations of responses of different bedding structures of shale to ultrasonic wave were carried out by using the second order in time and fourth order in space grid finite difference method, based on the grey system theory, sensitive factors of acoustic parameters of bedding structure were selected, and the dynamic mechanical parameter model of bedded shale was established, which was verified by the ultrasonic transmission experiment results on core down Well ZY1 and YY1 and the logging data of Well ZY2. The results show that: (1) The correlation coefficient between analog and experimental waveforms is greater than 80%, indicating that the numerical simulation method can effectively simulate ultrasonic transmission experiment. (2) Acoustic velocity is a conventional sensitive factor used to characterize shale bedding structure, whereas the attenuation coefficient is sensitive to the change of bedding thickness, with correlation coefficient of 0.89, therefore, using the normalized results of attenuation coefficient to comprehensively describe the shale bedding can make the results more accurate. (3) The correlation between the dynamic and static parameters calculated by the model is better than that of the traditional model; and the predicted values of rock mechanics obtained by using the model and logging data inversion are in good agreement with the experimental values. The research results lay the foundation for further accurate prediction of rock mechanic parameters using sonic logging data.

Dynamic specification tests for dynamic factor models

SummaryWe derive computationally simple expressions for score tests of misspecification in parametric dynamic factor models using frequency domain techniques. We interpret those diagnostics as time domain moment tests which assess whether certain autocovariances of the smoothed latent variables match their theoretical values under the null of correct model specification. We also reinterpret reduced‐form residual tests as checking specific restrictions on structural parameters. Our Gaussian tests are robust to nonnormal, independent innovations. Monte Carlo exercises confirm the finite‐sample reliability and power of our proposals. Finally, we illustrate their empirical usefulness in an application that constructs a US coincident indicator.

Local Lagged Adapted Generalized Method of Moments: An Innovative Estimation and Forecasting Approach and its Applications

Abstract In this work, an attempt is made to apply the Local Lagged Adapted Generalized Method of Moments (LLGMM) to estimate state and parameters in stochastic differential dynamic models. The development of LLGMM is motivated by parameter and state estimation problems in continuous-time nonlinear and non-stationary stochastic dynamic model validation problems in biological, chemical, engineering, energy commodity markets, financial, medical, military, physical sciences and social sciences. The byproducts of this innovative approach (LLGMM) are the balance between model specification and model prescription of continuous-time dynamic process and the development of discrete-time interconnected dynamic model of local sample mean and variance statistic process (DTIDMLSMVSP). Moreover, LLGMM is a dynamic non-parametric method. The DTIDMLSMVSP is an alternative approach to the GARCH(1,1) model, and it provides an iterative scheme for updating statistic coefficients in a system of generalized method of moment/observation equations. Furthermore, applications of LLGMM to energy commodities price, U.S. Treasury Bill interest rate and the U.S.–U.K. foreign exchange rate data strongly exhibit its unique role, scope and performance, in particular, in forecasting and confidence-interval problems in applied statistics.

Nonlinear inversion method based on dynamics model modification

In the fields of modern aviation system, subgrade vehicle system and complex mechanical system, there is a problem that parameters of most dynamic models are inaccurate. This problem results in a large difference between the model results and the experimental results. In order to solve this problem, this paper build a nonlinear inversion method based on dynamics model modification (NIDM). Firstly, the error relationship was obtained by integrating the experimental data with the simulation results of the forward modelling model by the cost function and penalty function. Then, the problem of error function minimization was solved by using the parameter iteration generated by particle swarm optimization algorithm, and the corrected parameters of the forward modelling model were obtained. Finally, the method was tested by building a vehicle suspension vibration model and a pavement excitation model as test samples. The test results show that the fitting degree between the simulation results and the experimental results can be effectively improved by modifying the parameters of the dynamic model based on the NIDM method.

Goodness-of-Fit for Regime-Switching Copula Models with Application to Option Pricing

We consider several time series and for each of them, we fit an appropriate dynamic parametric model. This produces serially independent error terms for each time series. The dependence between these error terms is then modeled by a regime-switching copula. The EM algorithm is used for estimating the parameters and a sequential goodness-of- fit procedure based on Cramer-von Mises statistics is proposed to select the appropriate number of regimes. Numerical experiments are performed to assess the validity of the proposed methodology. As an example of application, we evaluate a European put-on-max option on the returns of two assets. In order to facilitate the use of our methodology, we have built a R package HMMcopula available on CRAN.

A dynamic parameter identification method of industrial robots considering joint elasticity

Considering the joint elasticity, a novel dynamic parameter identification method is proposed for general industrial robots only with motor encoders. Firstly, the unknown parameters of the elastic joint dynamic model are analyzed and divided into two types. The first type is the motion-independent parameter only including the joint stiffness, which can be identified by the static force/torque-deformation experiments without the dynamic model. The second type is the motion-dependent parameter composed of the rest of the parameters, which needs the dynamic excitation experiments. Therefore, these two types of parameters can be identified separately. Meanwhile, it is found that the rotor inertia parameters can be obtained from the manufacturer, which reduces the identification difficulty of other parameters. After obtaining the rotor inertia and joint stiffness, an approximate processing algorithm is proposed considering the motor friction to establish the linear identification model of other parameters. Hence, the least squares can be employed to identify the parameters, and the independence of the inertia and joint viscous friction parameters are not affected. Meanwhile, the exciting trajectories can be optimized throughout the robot workspace, which reduces the effect of measurement noise on identification accuracy. With the proposed separated identification strategy and approximate processing algorithm, the dynamic parameters can be obtained precisely without double encoders on each joint. Finally, a series of simulations are conducted to evaluate the good performance of the proposed method.

Estimation of physical parameters under location uncertainty using an ensemble2–expectation–maximization algorithm

Estimating the parameters of geophysical dynamic models is an important task in the data assimilation (DA) techniques used to forecast initialization and reanalysis. In the past, most parameter estimation strategies were derived by state augmentation, yielding algorithms that are easy to implement but may exhibit convergence difficulties. The expectation–maximization (EM) algorithm is considered advantageous because it employs two iterative steps to estimate the model state and the model parameter separately. In this work, we propose a novel ensemble formulation of the maximization step in EM that allows a direct optimal estimation of the physical parameters using iterative methods for linear systems. This departs from current EM formulations that are only capable of dealing with additive model error structures. This contribution shows how the EM technique can be used for dynamics identification problems with the model error parameterized as an arbitrary complex form. The proposed technique is used here for the identification of stochastic subgrid terms that account for processes unresolved by a geophysical fluid model. This method, together with the augmented state technique, is evaluated to estimate such subgrid terms through high‐resolution data. As compared to the augmented state technique, our method is shown to yield considerably more accurate parameters. In addition, in terms of prediction capacity, it leads to a smaller generalization error as a result of the overfitting of the trained model on the presented data and eventually better forecasts.

Градиентные методы оптимизации в машинном обучении идентификации параметров динамических систем

The article considers one of the possible ways to solve the problem of estimating the unknown parameters of dynamic models described by differential-algebraic equations. Parameters are estimated based on the results of observations of the behavior of the mathematical model. Their values are found as a result of minimizing the criterion that describes the total quadratic deviation of the state vector coordinates from the exact values obtained at measurements at different points in time. The parallelepiped type constraints are imposed on the parameter values. To solve the optimization problem, it is proposed to use gradient optimization methods used in machine learning procedures: the stochastic gradient descent method, the classical moment method, the Nesterov accelerated gradient method, the adaptive gradient method, root mean square propagation method, the adaptive moment estimation method, the adaptive estimation method modification, Nesterov–accelerated adaptive moment estimation method. An example of identification of the parameters of a linear mathematical model describing a change in the characteristics of a chemical process is shown, which demonstrates the comparative effectiveness of the optimization methods of the selected group. The methods used to search for an extremum do not guarantee finding a result – a global extremum, but allow you to get a solution of good enough quality for an acceptable time. The results of calculations by all the listed optimization methods are presented. Recommendations on the selection of method parameters are given. The obtained numerical results demonstrated the effectiveness of the proposed approach. The found approximate values of the estimated parameters slightly differ from the best known solutions obtained by other methods.