This paper applies genetic algorithms to the problem of induction motor parameter determination. Generally available manufacturers published data like starting torque, breakdown torque, full load torque, full load power factor etc, are used to determine the motor parameters for subsequent use in studying machine transients. Results from several versions of the genetic algorithm are presented as well as a comparison with the Newton-Raphson method.

This paper applies genetic algorithms to the problem of induction motor parameter determination. Generally available manufacturers published data like starting torque, breakdown torque, full load torque, full load power factor etc, are used to determine the motor parameters for subsequent use in studying machine transients. Results from several versions of the genetic algorithm are presented as well as a comparison with the Newton-Raphson method.

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It is ofton necessary to determine the electrical parameters of hwdrods of different motors in an industrial plant for system studies. Electrical parameters can be obtained by actually testing each of the motors, which is undoubtedly a tedious and time consuming task, if not impossible, due to the requirement of continuous operation of motors in the process industry, for example.The determination of the electrical parameters of motors can be accomplished more economically with the help of a new optimization t e c h n i q u e called Genetic Algorithms. This method is widely accepted for its simplicity, ability to converge with minimal input from the user with a short computational time.

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