Efficient Estimation of Electrical Machine Behavior by Model Order Reduction

2021/03/31 by Fabian Müller, Fabian Muller, Andreas Siokos +3
Physics and Astronomy · Materials Science · Engineering · #Model Reduction and Neural Networks #Magnetic Properties and Applications #Real-time simulation and control systems

paper · doi:10.1109/tmag.2021.3070183

Abstract

The proper orthogonal decomposition (POD) is an efficient model order reduction method, which is frequently coupled with the discrete empirical interpolation method (DEIM) to solve nonlinear electromagnetic problems. A drawback of this method is that instabilities can occur related to the reduction operator of the nonlinear part. In this contribution, different DEIMs and the Gappy POD are employed and analyzed. Consecutively, the methods are employed to efficiently estimate the behavior of a permanent magnet synchronous machine in terms of global quantities, such as torque and iron losses.

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