Yannick Paquay

Reduced-Order Model Accounting for High-Frequency Effects in Power Electronic Components

This paper proposes a reduced-order (RO) model of power electronic components based on the proper orthogonal decomposition. Starting from a full-wave finite-element model and several snapshots (solutions at different frequencies), the RO model is constructed. Local field values (e.g., magnetic flux density, electric current density, magnetic field, or electric field) and global quantities (e.g., characteristic complex impedance and Joule losses) can be determined for the intermediate frequencies with a very low computational cost and high accuracy. Particular attention is paid to the choice of the most suitable snapshots by means of three different greedy algorithms, the performance of which is compared. We adopt an automatic greedy algorithm that depends only on the RO model.

Proper orthogonal decomposition versus Krylov subspace methods in reduced-order energy-converter models

In this paper, the proper orthogonal decomposition and the Arnoldi-based Krylov subspace methods are applied to the magnetodynamic finite element analysis of power electronic converters. The performance of these two model order reduction techniques is compared both in frequency and time domain. Moreover, two original, adaptive and automated greedy snapshots selection methods are investigated using either local or global quantities for selecting the snapshots (frequencies or time steps).

Nonlinear Interpolation on Manifold of Reduced-Order Models in Magnetodynamic Problems

The proper orthogonal decomposition (POD) is an efficient model-order reduction (MOR) technique for linear problems in computational sciences, recently gaining popularity in electromagnetics. However, its efficiency has been shown to considerably degrade for nonlinear problems. In this paper, we propose a method to reduce nonlinear magnetodynamic problems by combining POD with an interpolation on manifolds, which interpolates the reduced bases to efficiently construct the appropriate reduced model. This method, new in electromagnetics, is applied on an inductor-core system and showed good results compared with the classical MOR approaches, e.g., direct POD reduction and a standard interpolation of pre-computed reduced bases (i.e., Lagrange interpolation).

Model Order Reduction of Nonlinear Eddy Current Problems Using Missing Point Estimation

In electromagnetics, the finite element method has become the most used tool to study several applications from transformers and rotating machines in low frequencies to antennas and photonic devices in high frequencies. Unfortunately, this approach usually leads to (very) large systems of equations and is thus very computationally demanding. This contribution compares three model order reduction techniques for the solution of nonlinear low frequency electromagnetic applications (in the so-called magnetoquasistatic regime) to efficiently reduce the number of equations—leading to smaller and faster systems to solve.

Nonlinear reduced order model of a 3-phase transformer for electric network simulator coupling

Model order reduction approaches have been extensively studied in the context of numerous linear dynamical systems. One of the most famous technique, initially used in mechanics and becoming increasingly popular in electromagnetics, is the Proper Orthogonal Decomposition (POD), which uses a set of full order solutions to derive an optimal reduced basis. However, using only this technique does not allow to reduce any nonlinear parts of the model due to a constant need of the full size solution — requiring the expanding of the reduced states at each evaluation. Therefore, most of the gain is lost. In this contribution we combine the POD with the Missing Point Estimation (MPE), a general (nonlinear) reduction approach, to achieve excellent reduction ratios. The obtained reduced model is then coupled with an electric network simulator, transforming expensive, high fidelity finite element models into lighter equivalents amenable to fast evaluations.