Generalization of the Neville–Aitken interpolation algorithm on Grassmann manifolds: Applications to reduced order model

Transmission Line model are an important role in the electrical power supply. Modeling of such system remains a challenge for simulations are necessary for designing and controlling modern power systems.In order to analyze the numerical approach for a benchmark collection Comprehensive of some needful real-world examples, which can be utilized to evaluate and compare mathematical approaches for model reduction. The approach is based on retaining the dominant modes of the system and truncation comparatively the less significant once.as the reduced order model has been derived from retaining the dominate modes of the large-scale stable system, the reduction preserves the stability. The strong demerit of the many MOR methods is that, the steady state values of the reduced order model does not match with the higher order systems. This drawback has been try to eliminated through the Different MOR method using sssMOR tools. This makes it possible for a new assessment of the error system Offered that the Observability Gramian of the original system has as soon as been thought about, an H∞ and H2 error bound can be calculated with minimal numerical effort for any minimized model attributable to The reduced order model (ROM) of a large-scale dynamical system is essential to effortlessness the study of the system utilizing approximation Algorithms. The response evaluation is considered in terms of response constraints and graphical assessments. the application of Approximation methods is offered for arising ROM of the large-scale LTI systems which consist of benchmark problems. The time response of approximated system, assessed by the proposed method, is also shown which is excellent matching of the response of original system when compared to the response of other existing approaches .

Investigating Pole behavior of Reduced Order Model

SSRN Electronic Journal ◽

10.2139/ssrn.3463658 ◽

2019 ◽

Author(s):

Puneet Sharma ◽

S. K. Jha

Keyword(s):

Reduced Order Model ◽

Order Model ◽

Reduced Order

Theoretical and practical identifiability of a reduced order model in an activated sludge process doing nitrification and denitrification

Water Science & Technology ◽

10.2166/wst.1998.0556 ◽

1998 ◽

Vol 37 (12) ◽

pp. 309-316 ◽

Cited By ~ 4

Author(s):

S. Julien ◽

J. P. Babary ◽

P. Lessard

Keyword(s):

Activated Sludge ◽

Reduced Order Model ◽

Actual Behavior ◽

Activated Sludge Process ◽

Order Model ◽

Identifiability Analysis ◽

Reduced Order ◽

3 Dimensional ◽

Nitrification And Denitrification ◽

Line Oxygen

This paper deals with the structural identifiability and the identification of the parameters of a reduced order model used for control of a single reactor activated sludge process doing nitrification and denitrification. This reduced order model is splitted into two submodels, one 3-dimensional state submodel in aerobic conditions and one 2-dimensional state submodel in anoxic conditions. The identifiability analysis is based on on-line oxygen and nitrate concentrations data. It has been shown that the reduced order model is structurally identifiable. The parameter identification has been carried out by using the simplex method of Nelder and Mead. Simulation results performed over a range of six hours (two aerobic/anoxic cycles), show that there exists a good fit between the simulated solution and the actual behavior of a lab scale pilot plant.

Comparison of stochastic reduced order model and stochastic collocation methods for the sensitivity analysis of crosstalk

Tenth International Conference on Computational Electromagnetics (CEM 2019) ◽

10.1049/cp.2019.0118 ◽

2019 ◽

Author(s):

Zhouxiang Fei ◽

Yi Huang ◽

Jiafeng Zhou ◽

Chaoyun Song

Keyword(s):

Sensitivity Analysis ◽

Reduced Order Model ◽

Collocation Methods ◽

Stochastic Collocation ◽

Order Model ◽

Reduced Order ◽

Stochastic Collocation Methods