Parameters Identification of Induction Motor Model Based on Manufacturer Data and Sequential Quadratic Programming

Le résumé fourni par la source

In order to precaution and control the transient voltage stability of the receiving-end system, it is very necessary to quickly and accurately calculate the model parameters for induction motor synthesis load. There are several methods to obtain the model parameters of induction motor, in which, estimating the model parameters of induction motor using for power system stability analysis according to the nameplate data is a significant promising approach with potential application. In this paper, a new optimized mathematical model for identification of induction motor single-cage and double-cage parameters are proposed, it’s overcome the deficiency of artificially adding approximate constraints in parameter identification of induction motor models from induction motor manufacturer data. Minimization of the induction motor efficiency deviation is taken as the goal and important induction motor performance indicators, such as the stator current, the input reactive power, the maximum electromagnetic torque and the starting parameters, are equal to their manufacturer values are regarded as constraints, the sequential quadratic programming (SQP) is used to solve the nonlinear problem. The proposed new mathematical model and algorithm were verifed on a sample of 6 induction motors of different capacity, manufacturers, and rated voltage. The induction motor performance characteristics supplied by the manufacturer and used to identification parameters of induction motor are then calculated, using the equivalent circuit estimated parameters themselves. In all the studied cases, the calculated induction motors performance indexes are found to be in excellent agreement with the manufacturer data. Comparison with other methods shows that the induction motor model parameters obtained by this method can reflect the working characteristics of induction motor single-cage and double-cage model more accurately.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.