Estimation of induction motor single-cage model parameters from manufacturer data

This paper proposes a new method for estimating the induction motor single-cage model parameters from the manufacturer data. A multidimensional single-objective nonlinear optimization problem is formulated to minimize the deviation between the values of the performance characteristics provided by the manufacturer and their corresponding estimates. By introducing variable slip dependency parameters in the optimization problem, the proposed method gives a single-cage motor model that is capable of simultaneously predicting the induction motor characteristics at high and low slips, both with high accuracy. The proposed method has been tested on a sample of eight induction motors of different sizes, rated voltages and manufacturers. The results show the effectiveness of the proposed method in providing single-cage induction motor models that are capable of accurately estimating the different motor external quantities along the entire slip domain.