Abstract
The appropriate efficiency of a dual stator winding squirrel-cage induction motor (DSWIM) is obtained when the ratio of two frequencies feeding the machine is equal to the ratio of the number of poles. In the vector control method, the estimation of flux at low speed is difficult. To solve this problem, researchers have benefited from the free capacity of the two windings of the stator. This makes the motor deviate from its standard operating mode at low speed. The main purpose of the present study is to reduce the power losses of the inverter units in the sensorless DSWIM drive using the proposed control scheme at low speed. In this control scheme, the speed is estimated based on the modified intelligent model reference adaptive system (MIMRAS) without estimating the stator resistance at low speed. The proposed methods were simulated in MATLAB/Simulink software, and the results of simulation confirmed the assumptions.
Highlights
The dual stator winding induction motor (DSWIM) studied in this paper consists of a standard squirrel-cage rotor and a stator with two separate symmetric three-phase windings with different numbers of poles
The simulation of the dual stator winding squirrel-cage induction motor drive was performed in MATLAB/Simulink to evaluate the proposed ideas
Simulation results of the conventional method In the conventional model, the frequency is usually held constant at a minimum value in the first winding, the second winding can work in either motor or generator operating mode at low speed
Summary
The dual stator winding induction motor (DSWIM) studied in this paper consists of a standard squirrel-cage rotor and a stator with two separate symmetric three-phase windings with different numbers of poles. The direct and indirect vector control methods depend on flux estimation [4,5,6]. In the indirect vector control method, the calculation of the slip frequency depends on the rotor resistance. In the direct vector control, at low speed, the estimation of flux has noticeable sensitivity because the voltage drop on the stator resistance is comparable to the input stator voltage. In [6], the rotor resistance was estimated using a novel Flux-MRAS scheme based on artificial neural network in the indirect vector control method. In [10,11], different kinds of reformed extended Kalman filter (EKF) algorithms were presented for improving flux and torque estimation
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