Sensitivity analysis for estimation of power losses in magnetic cores using neural network

Abstract

Abstract Experimental data from a sample of 42 cores made from grain oriented 0.27 mm thick 3% SiFe electrical steel with dimensions ranging from 35 to 160 mm outer diameter, 25–100 mm inner diameter and 10–70 mm strip width and a flux density range 0.2–1.7 T have been obtained at 50 Hz and used as training data to a feed forward neural network. An analytical equation for prediction of power loss as depends on input parameters from the results of sensitivity analysis has been obtained. The calculated power losses with the analytical expression have also been compared with power loss obtained from the Preisach model after it has been applied to toroidal cores. The results show the proposed model can be used for estimation of power losses in the toroidal cores.