Shuffled frog-leaping algorithm for parameter estimation of a double-cage asynchronous machine

Shuffled frog-leaping algorithm for parameter estimation of a double-cage asynchronous machine

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This study introduces a shuffled frog-leaping algorithm based method for the estimation of induction motor double-cage model parameters from standard manufacturer data: full load torque, full load power factor, full load current, maximum torque, starting torque and starting current. The steady-state equivalent circuit is applied for the simulations. The circuit parameters are found as the result of the error minimisation function between the estimated and maker data. The suggested algorithm solves the parameter estimation problem and surpasses the solutions reached by particle swarm optimisation, genetic algorithms and classical parameter estimation method (a modified Newton method). The algorithm has been tested on three motors.


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