Application of Gröbner Basis Algorithm for Estimating Induction Motors Parameters

Abstract

This paper proposes a method for estimating the equivalent circuit parameters of an induction motor from manufacturer’s datasheet information using the Gröbner basis algorithm. Induction motor parameters are crucial for motor control and performance monitoring, yet they are often not readily available from manufacturers. To address this issue, an approach is developed that minimizes the normalized error between the computed performance of the equivalent circuit and the manufacturer’s specifications. The Gröbner basis algorithm is implemented in MATLAB to facilitate parameter estimation. The method is tested on 5 hp, 50 hp, and 500 hp sample motors and the results are compared with Genetic Algorithm and Newton-Raphson methods. Findings show that the Gröbner basis approach achieves comparable accuracy to these established methods, with percentage errors generally below 7% for key parameters like rotor resistance, stator resistance, and leakage impedance. Notably, the Gröbner basis method requires no iterations, overcoming the slow convergence issues associated with the other techniques. This study demonstrates that the Gröbner basis algorithm offers an effective, non-iterative solution for estimating induction motor parameters with moderate accuracy, providing a valuable alternative to conventional approaches.