Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Modified direct torque control for single-phase induction motor drives
WSEAS Transactions on Circuits and Systems
WSEAS Transactions on Circuits and Systems
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This paper presents a genetic-based approach to correct the parameters for single-phase induction motors in various supply voltage levels. From conventional tests, electrical (resistances and inductances) and mechanical (moment of inertia and damping coefficient) parameters of the stator and the rotor can be estimated. The set of obtained parameters is able to apply for steady-state performance analyses. In transient states, motor responses generated by these parameters are not met the condition of acceptable accuracy. By some efficient search method incorporate with experimental data, the obtained parameters can be refined to yield the best curve fitting in both transient and steady-state responses. A 0.37-kW, 220 V, 50 Hz single-phase induction motor was used for test to verify the effectiveness of the proposed algorithm. Furthermore, with six different motor supply voltages, voltage-dependent parameters of single-phase induction motors can be established. As a result, the voltage-dependent parameters of the induction motors can be satisfactorily improved to represent the motor dynamic in various supply voltages.