Analysis of Parameter Variability in an Integrated Wireless Power Transfer System via Partial Least-Squares Regression

This article deals with the application of the partial least-squares (PLS) regression to the uncertainty quantification of an integrated wireless power transfer with 30 random variables. It considers the development of surrogate models using a limited set of training samples in order to estimate statistical quantities of the converter efficiency with a relatively low computational cost compared with the standard brute-force Monte Carlo (MC) simulation. The strength, the performance, and the features of the proposed modeling approach are then compared with the ones of an advanced probabilistic surrogate model combining the least-squares support vector machine (LS-SVM) and the Gaussian process regression (GPR). The MC simulation is considered as a reference.