The induction motors are largely used in several industry sectors. The dimensioning of an induction motor has still been inaccurate because in most of the cases the load behavior in its shaft is completely unknown. The proposal of this paper is to use artificial neural networks as tool for dimensioning of induction motors rather than conventional methods, which use classical identification techniques and mechanical load modeling. Simulation results are also presented to validate the proposed approach.0-7803-7278-6/02/$10.00 02002 IEEE
An electrical and thermal mathematical dynamic model for a three-phase squirrel-cage induction motor is described to evaluate, via microcomputer, stator winding, rotor winding, stator core and rotor core instantaneous temperature rises for motors operating fed from sinusoidal and nonsinusoidal (inverter) voltage power supplies and driving continuous and intermittent load duties, including startings and brakings. It is required regular manufacturer bulletin data and also stator core, stator winding, rotor core and rotor winding masses. The model takes into account the skin effect on rotor parameters and the changings of the winding electrical resistances with the respective machine part temperatures.
This work presents the design of a neurofuzzy controller with simplified architecture that minimizes the processing time used in several stages associated with systems and processes modelling. The basic procedures of fuzzification and defuzzification are very simplified, whereas the inference procedures are computed in a direct way. The simplified architecture has allowed a fast and easy configuration of the neurofuzzy controller, as consequence, the control rules that define the control actions are obtained automatically. To validate the proposed approach, this neurofuzzy system is used in an industrial application for fluid flow control.
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