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Uncertainty quantification and sensitivity analysis in electrical machines with stochastically varying machine parameters

Communication avec acte
Author
OFFERMANN, Peter
MAC, Hung
NGUYEN, Thu Trang
13338 Laboratoire d’Électrotechnique et d’Électronique de Puissance - ULR 2697 [L2EP]
CLENET, Stéphane
13338 Laboratoire d’Électrotechnique et d’Électronique de Puissance - ULR 2697 [L2EP]
DE GERSEM, Herbert
HAMEYER, Kay

URI
http://hdl.handle.net/10985/9556
DOI
10.1109/TMAG.2014.2354511
Date
2015

Abstract

Electrical machines that are produced in mass production suffer from stochastic deviations introduced during the production process. These variations can cause undesired and unanticipated side-effects. Until now, only worst case analysis and Monte-Carlo simulation have been used to predict such stochastic effects and reduce their influence on the machine behavior. However, these methods have proven to be either inaccurate or very slow. This paper presents the application of a polynomialchaos meta-modeling at the example of stochastically varying stator deformations in a permanent-magnet synchronous machine. The applied methodology allows a faster or more accurate uncertainty propagation with the benefit of a zero-cost calculation of sensitivity indices, eventually enabling an easier creation of stochastic insensitive, hence robust designs.

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