Abstract
This paper deals with the implementation of a new technique of stochastic search to find the best set of parameters in a mathematical model, applied to the single cage (SC) model of the induction motor (IM). The technique includes a new strategy to generate variable constraints of the domain, seven error functions, weight for the operating zones of the IM, and multi-objective functions. The results are validated with experimental data of the torque and current in an IM, and show better fitting to the experimental curves compared with the results of two different techniques, one deterministic and the other one stochastic. The results obtained allow us to conclude that the best set of parameters for the model depends on the weights assigned to the objective functions and to the operating zones.
Highlights
Many implementations use pre-established models of electromechanical systems for design, induction motor (IM) design, protection system, speed and/or torque control systems, profile tracking systems, structural fault analysis, mechanisms of recovery in case of failures in the electrical power grid, and adaptive control, among others
The implementation of the stochastic search technique with variables deterministic constraints (SSVDCs) technique was validated with the experimental data of torque and current extracted from [33] that correspond to a three-phase induction motor of 75 kW, with a squirrel cage rotor, 3300 V supply, 50 Hz, rated current = 13.62 A, rated torque = 401.5
The analysis of the results of the implementation of the SSVDC technique will be carried out in three steps: First, the results will be analyzed by varying the weights of the two objective functions; second, the weights of the three operating zones will be varied; and third, the SSVDC technique will be compared with two other optimization techniques
Summary
Many implementations use pre-established models of electromechanical systems for design, induction motor (IM) design, protection system, speed and/or torque control systems, profile tracking systems, structural fault analysis (short-circuit in the windings of the stator), mechanisms of recovery in case of failures in the electrical power grid, and adaptive control, among others. Other applications demand that the technique estimates a set of parameters that generates a minimal validation error, only taking into account the effect of the set of parameters instead of the individual value of each parameter Such is the case of the single cage model of the IM, which needs a set of parameters to generate the curves of the torque and current.
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