An improved state-space averaged model of a dual active bridge converter for use in acausal system modeling

2016 ◽  
Author(s):  
Seth Cooper ◽  
Andrew Klem ◽  
M. Hashem Nehrir ◽  
Hongwei Ga
Keyword(s):  
State Space ◽  
System Modeling ◽  
Dual Active Bridge ◽  
Averaged Model

scholarly journals State-Space Model of Quasi-Z-Source Inverter-PV Systems for Transient Dynamics Studies and Network Stability Assessment

Energies ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 4150
Author(s):  
Lluís Monjo ◽  
Luis Sainz ◽  
Juan José Mesas ◽  
Joaquín Pedra
Keyword(s):  
Power Systems ◽  
State Space ◽  
State Space Model ◽  
Pv System ◽  
Pv Systems ◽  
Renewable Power ◽  
Ac Networks ◽  
Transient Interactions ◽  
The Stability ◽  
Averaged Model

Photovoltaic (PV) power systems are increasingly being used as renewable power generation sources. Quasi-Z-source inverters (qZSI) are a recent, high-potential technology that can be used to integrate PV power systems into AC networks. Simultaneously, concerns regarding the stability of PV power systems are increasing. Converters reduce the damping of grid-connected converter systems, leading to instability. Several studies have analyzed the stability and dynamics of qZSI, although the characterization of qZSI-PV system dynamics in order to study transient interactions and stability has not yet been properly completed. This paper contributes a small-signal, state-space-averaged model of qZSI-PV systems in order to study these issues. The model is also applied to investigate the stability of PV power systems by analyzing the influence of system parameters. Moreover, solutions to mitigate the instabilities are proposed and the stability is verified using PSCAD time domain simulations.

Optimal Scheduling of Automatic Guided Vehicle System via State Space Realization

2013 ◽  
Vol 7 (5) ◽  
pp. 571-580 ◽  
Author(s):  
Kenji Sawada ◽  
◽  
Seiichi Shin ◽  
Kenji Kumagai ◽  
Hisato Yoneda ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
State Space ◽  
Predictive Control ◽  
Transfer Problem ◽  
System Modeling ◽  
Optimal Scheduling ◽  
Throughput Maximization ◽  
State Space Realization ◽  
Automatic Guided Vehicle ◽  
Space Realization

This paper considers dynamical system modeling of transportation systems in semiconductor manufacturing based on state space realization. Utilizing this method, we consider an optimal scheduling problem for an Automatic Guided Vehicle (AGV) transfer problem, which is to control AGV congestion at transport rail junctions. Our scheduling algorithm is based on model-predictive control in which the cycle of measurement, prediction and optimization is repeated. Its optimization is recast as an Integer Linear Programming (ILP) problem. Since little attention has been given to AGV scheduling based on model-predictive control, no method is, to our knowledge, known for determining appropriate cost functions. Here, we focus on throughput maximization and shortest transit time problems and show corresponding cost function settings. We also propose a visualization algorithm of AGV scheduling via state space realization, presenting numerical examples.

STATE SPACE MODELING OF YEAST GENE EXPRESSION DYNAMICS

2007 ◽  
Vol 05 (01) ◽  
pp. 31-46 ◽  
Author(s):  
OLLI HAAVISTO ◽  
HEIKKI HYÖTYNIEMI ◽  
CHRISTOPHE ROOS
Keyword(s):  
Gene Expression ◽  
State Space ◽  
Dynamic Models ◽  
System Modeling ◽  
Dynamic State ◽  
Yeast Genome ◽  
High Dimensional ◽  
Dimensional System ◽  
Yeast Saccharomyces Cerevisiae ◽  
Space Modeling

Combined interaction of all the genes forms a central part of the functional system of a cell. Thus, especially the data-based modeling of the gene expression network is currently one of the main challenges in the field of systems biology. However, the problem is an extremely high-dimensional and complex one, so that normal identification methods are usually not applicable specially if aiming at dynamic models. We propose in this paper a subspace identification approach, which is well suited for high-dimensional system modeling and the presented modified version can also handle the underdetermined case with less data samples than variables (genes). The algorithm is applied to two public stress-response data sets collected from yeast Saccharomyces cerevisiae. The obtained dynamic state space model is tested by comparing the simulation results with the measured data. It is shown that the identified model can relatively well describe the dynamics of the general stress-related changes in the expression of the complete yeast genome. However, it seems inevitable that more precise modeling of the dynamics of the whole genome would require experiments especially designed for systemic modeling.

A Comparison Between Two Average Modelling Techniques of AC-AC Power Converters

2015 ◽  
Vol 6 (1) ◽  
pp. 32 ◽  
Author(s):  
Pawel Szczesniak
Keyword(s):  
State Space ◽  
Comparative Evaluation ◽  
Power Converters ◽  
Frequency Converter ◽  
Averaging Technique ◽  
Ac Power ◽  
Modelling Techniques ◽  
Averaged Model

In this paper, a comparative evaluation of two modelling tools for switching AC-AC power converters is presented. Both of them are based on average modelling techniques. The first approach is based on the circuit averaging technique and consists in the topological manipulations, applied to a converters states. The second approach makes use of state-space averaged model of the converter and is based on analytical manipulations using the different state representations of a converter. The two modelling techniques are applied to a same AC-AC converter called matrix-reactance frequency converter based on buck-boost topology. These techniques are compared on the basis of their rapidity, quantity of calculations and transformations and its limitations.

Recursive Immuno-Inspired Algorithm for Time Variant Discrete Multivariable Dynamic System State Space Identification

2015 ◽  
Vol 5 (2) ◽  
pp. 69-100 ◽  
Author(s):  
Mateus Giesbrecht ◽  
Celso Pascoli Bottura
Keyword(s):  
Dynamic System ◽  
State Space ◽  
System Modeling ◽  
State Space Model ◽  
Small Time ◽  
Time Interval ◽  
System State ◽  
Space Model ◽  
Starting Point ◽  
Benchmark System

In this paper a recursive immuno inspired algorithm is proposed to identify time variant discrete multivariable dynamic systems. The main contribution of this paper has as starting point the idea that a multivariable dynamic system state space model can be seen as a point in a space defined by all possible matrices quadruples that define a state space model. With this in mind, the time variant discrete multivariable dynamic system modeling is transformed in an optimization problem and this problem is solved with an immuno inspired algorithm. To do that the inputs given to the system and the resulting outputs are divided in small sets containing data from small time intervals. These sets are defined as time windows, and for each window an immuno inspired optimization algorithm is applied to find the state space model that better represents the system at that time interval. The initial candidate solutions of each time interval are the ones of the last interval. The immuno inspired algorithm proposed in this paper has some modifications to the original Opt-AINet algorithm to deal with the constraints that are natural from the system identification problem and these modifications are also contributions of this paper. The method proposed in this paper was applied to identify a time variant benchmark system, resulting in a time variant model. The outputs estimated with this model are closer to the benchmark system outputs than the outputs estimated with models obtained by other known identification methods. The Markov parameters of the variant benchmark system are also reproduced by the time variant model found with the new method.

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