Orthogonal Interpolation Method for Order Reduction of a Synchronous Machine Model

Far, Mehrnaz Farzam; Martin, Floran; Belahcen, Anouar; Montier, Laurent; Henneron, Thomas

doi:10.1109/tmag.2017.2768328

Cited by 17 publications

(6 citation statements)

References 22 publications

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“…To tackle this issue, POD and Interpolation (POD+I) 18‐20 aims at approximating the POD coefficients using surrogate models. Once the POD basis is assembled, a regression‐based approach is used to establish a mapping from design variables to projection coefficients onto the POD basis.…”

Section: Introductionmentioning

confidence: 99%

Disciplinary proper orthogonal decomposition and interpolation for the resolution of parameterized multidisciplinary analysis

Berthelin

Dubreuil

Salaün

et al. 2022

Numerical Meth Engineering

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This article proposes a new method for the resolution of parameterized coupled systems of equations, such as typical in multidisciplinary analysis (MDA).It is based on the use of disciplinary surrogate models within a multi-query context. The main idea is to replace the costly disciplinary solvers of the MDA by Proper Orthogonal Decomposition and Interpolation models. The main challenge we address is the high-dimensional coupling variables whose ranges are unknown. To overcome this issue, a training strategy is developed by uncoupling the disciplinary solvers from the MDA context. This new surrogate MDA called Disciplinary Proper Orthogonal Decomposition and Interpolation is iteratively enriched to solve the analysis with an estimation of the error made by the surrogate models. The disciplinary surrogate model which has the most influence on this error is determined by a sensitivity analysis and thus enriched. This approach allows to uncouple the disciplinary solvers during the training and enrichment phases. The approach is applied to various aeroelastic problems of an aircraft wing and allows to reduce by a factor of 5 the mean number of disciplinary solver calls needed for the resolution of the MDA.

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Section: Introductionmentioning

confidence: 99%

Disciplinary proper orthogonal decomposition and interpolation for the resolution of parameterized multidisciplinary analysis

Berthelin

Dubreuil

Salaün

et al. 2022

Numerical Meth Engineering

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“…To overcome this problem, the interpolation method which does not solve the reduced equations has been proposed [5], [6]. In this method, the machine response for arbitrary input is obtained via interpolation of the basis vectors obtained by POD.…”

Section: Introductionmentioning

confidence: 99%

“…In this method, the machine response for arbitrary input is obtained via interpolation of the basis vectors obtained by POD. The nonlinear interpolation is applied to the Grassmann manifold in [5], while the interpolation is adopted for the righthand vector of the singular value decomposition in [6].…”

Section: Introductionmentioning

confidence: 99%

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Fast Analysis of Rotating Machine Using Simplified Model-Order Reduction Based on POD

2020

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This paper proposes a simplified model order reduction for the fast dynamic simulation of electric motors. The magnetic fields are snapshotted for different input currents at each mechanical angle to construct a data matrix. The basis vectors are then computed by the singular value decomposition applied to the data matrix. The interpolation along the mechanical angle is performed by the dynamic mode decomposition. Fast computation of the magnetic field for arbitrary input current and mechanical angle is performed through interpolation of the basis vectors in the space of input currents for the dynamic analysis of motors.

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“…In the transient analysis [1,2] of a distribute circuit model of a busbar [3][4][5][6][7], the order of equation sets is extremely high. Meanwhile, model order reduction (MOR) techniques [8], such as the proper orthogonal decomposition (POD)-based MOR approach [9][10][11][12], the data-driven MOR method [13], the nonlinear MOR technique [14], and the dynamic mode decomposition (DMD)-based MOR approach [15], have all been proven to be very effective in combating such a high complexity issue. Compared with these reduction methods, the structure-preserving reduced-order interconnect macromodeling (SPRIM) based on Krylov subspaces, built on the Arnoldi algorithm [16], can achieve a higher order reduction radio and accuracy for large multi-port Resistor-Capacitor-Inductor (RCL) circuits [17][18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

A Broadband Enhanced Structure-Preserving Reduced-Order Interconnect Macromodeling Method for Large-Scale Equation Sets of Transient Interconnect Circuit Problems

Wang

Yang

2020

Energies

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In the transient analysis of an engineering power electronics device, the order of its equivalent circuit model is excessive large. To eliminate this issue, some model order reduction (MOR) methods are proposed in the literature. Compared to other MOR methods, the structure-preserving reduced-order interconnect macromodeling (SPRIM) based on Krylov subspaces will achieve a higher reduction radio and precision for large multi-port Resistor-Capacitor-Inductor (RCL) circuits. However, for very wide band frequency transients, the performance of a Krylov subspace-based MOR method is not satisfactory. Moreover, the selection of the expansion point in this method has not been comprehensively studied in the literature. From this point of view, a broadband enhanced structure-preserving reduced-order interconnect macromodeling (SPRIM) method is proposed to reduce the order of equation sets of a transient interconnect circuit model. In addition, a method is introduced to determine the optimal expansion point at each frequency in the proposed method. The proposed method is validated by the numerical results on a transient problem of an insulated-gate bipolar transistor (IGBT)-based inverter busbar under different exciting conditions.

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Orthogonal Interpolation Method for Order Reduction of a Synchronous Machine Model

Cited by 17 publications

References 22 publications

Disciplinary proper orthogonal decomposition and interpolation for the resolution of parameterized multidisciplinary analysis

Disciplinary proper orthogonal decomposition and interpolation for the resolution of parameterized multidisciplinary analysis

Fast Analysis of Rotating Machine Using Simplified Model-Order Reduction Based on POD

A Broadband Enhanced Structure-Preserving Reduced-Order Interconnect Macromodeling Method for Large-Scale Equation Sets of Transient Interconnect Circuit Problems

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