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Uncertainty Quantification and Sensitivity Analysis in Electrical Machines With Stochastically Varying Machine Parameters

Year: 2015

Type: article

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 to 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 polynomial chaos metamodeling 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.

Source: IEEE Transactions on Magnetics

DOI: 10.1109/tmag.2014.2354511

Authors: Peter Offermann, Hung Mac, Thu Trang Nguyen, Stephane Clenet, Herbert De Gersem, Kay Hameyer