scholarly journals Model order reduction for Linear Noise Approximation using time-scale separation

2016 ◽  
Author(s):  
Narmada Herath ◽  
Domitilla Del Vecchio
Keyword(s):  
Time Scale ◽  
Model Order Reduction ◽  
Order Reduction ◽  
Scale Separation ◽  
Model Order ◽  
Time Scale Separation ◽  
Linear Noise Approximation

scholarly journals A Robust Computational Technique for Model Order Reduction of Two-Time-Scale Discrete Systems via Genetic Algorithms

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Othman M. K. Alsmadi ◽  
Zaer S. Abo-Hammour
Keyword(s):  
Genetic Algorithms ◽  
Time Scale ◽  
Model Order Reduction ◽  
Fitness Function ◽  
Order Reduction ◽  
Discrete Systems ◽  
Computational Technique ◽  
Model Order ◽  
New Approach ◽  
Reduced Order

A robust computational technique for model order reduction (MOR) of multi-time-scale discrete systems (single input single output (SISO) and multi-input multioutput (MIMO)) is presented in this paper. This work is motivated by the singular perturbation of multi-time-scale systems where some specific dynamics may not have significant influence on the overall system behavior. The new approach is proposed using genetic algorithms (GA) with the advantage of obtaining a reduced order model, maintaining the exact dominant dynamics in the reduced order, and minimizing the steady state error. The reduction process is performed by obtaining an upper triangular transformed matrix of the system state matrix defined in state space representation along with the elements ofB,C, andDmatrices. The GA computational procedure is based on maximizing the fitness function corresponding to the response deviation between the full and reduced order models. The proposed computational intelligence MOR method is compared to recently published work on MOR techniques where simulation results show the potential and advantages of the new approach.

scholarly journals Multi-Time Scale Model Order Reduction and Stability Consistency Certification of Inverter-Interfaced DG System in AC Microgrid

Energies ◽  
2018 ◽  
Vol 11 (1) ◽  
pp. 254 ◽  
Author(s):  
Xiaoxiao Meng ◽  
Qianggang Wang ◽  
Niancheng Zhou ◽  
Shuyan Xiao ◽  
Yuan Chi
Keyword(s):  
Time Scale ◽  
Model Order Reduction ◽  
Order Reduction ◽  
Scale Model ◽  
Model Order ◽  
Ac Microgrid

MULTI-TIME-SCALE SYSTEMS MODEL ORDER REDUCTION VIA GENETIC ALGORITHMS WITH EIGENVALUE PRESERVATION

2011 ◽  
Vol 20 (07) ◽  
pp. 1403-1418 ◽  
Author(s):  
ZÁER. S. ABO-HAMMOUR ◽  
OTHMAN M. K. ALSMADI ◽  
ADNAN M. AL-SMADI
Keyword(s):  
Genetic Algorithms ◽  
Time Scale ◽  
Model Order Reduction ◽  
Fitness Function ◽  
Order Reduction ◽  
New Technique ◽  
Dominant Eigenvalue ◽  
Order Model ◽  
Model Order ◽  
Systems Model

A novel substructure (dominant eigenvalue) preserving genetic algorithm approach for model order reduction (MOR) of multi-time-scale systems is presented in this paper. The new technique is formulated based on genetic algorithms (GAs), sub-optimization and estimation. The GA predicts the elements of an upper triangular matrix form of the system state matrix A, defined in state space representation along with the elements of B, C, and D matrices. The GA procedure is based on maximizing the fitness function corresponding to the reciprocal response deviation between the full order model and the reduced order model. The proposed GA model order reduction method is compared to well-known reduction techniques such as the Balanced Schur Decomposition (BSD), proper orthogonal decomposition (POD), and state elimination through balanced realization. Simulation results validate the robustness of the new technique for MOR with eigenvalue preservation.

THE NEW ALGORITHM OF SOLVING HARMONIC BALANCE EQUATIONS

2019 ◽  
Author(s):  
Vladimir Lantsov ◽  
A. Papulina
Keyword(s):  
Harmonic Balance ◽  
Model Order Reduction ◽  
Order Reduction ◽  
Balance Equations ◽  
Model Order ◽  
Cad Systems ◽  
Computational Costs ◽  
Reduction Methods ◽  
Model Equations

The new algorithm of solving harmonic balance equations which used in electronic CAD systems is presented. The new algorithm is based on implementation to harmonic balance equations the ideas of model order reduction methods. This algorithm allows significantly reduce the size of memory for storing of model equations and reduce of computational costs.

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