Generalized total least squares for identification of electromagnetic parameters of an induction motor

Abstract

This paper proposes an algorithm for electromagnetic parameters with errors in variables. The estimates obtained by the ordinary least squares (OLS) are biased due to errors in the variables. It is shown that even if there are errors in all variables with the same variance, the problem is reduced to generalized total least squares (GTLS). To find a solution to the generalized total least squares problem, an augmented system of equations is used that is equivalent to a biased normal system. This approach improves the conditionality of the system of equations being solved in comparison with the biased normal system and requires less memory compared to the solution based on the right singular vector. The simulation results show that the GTLS estimates are much more accurate than OLS estimates. Based on the proposed approach, recursive algorithms can be created for evaluating the parameters of an asynchronous electric motor online.