PROJECT TITLE :

Model-Predictive Flux Control of Induction Motor Drives With Switching Instant Optimization

ABSTRACT:

Typical model-predictive torque control (MPTC) requires tedious and time-consuming tuning work for stator flux weighting issue, and presents relatively high torque ripples. To solve these issues, this paper proposes a model-predictive flux management (MPFC) for two-level inverter-fed induction motor (IM) drives. The references of stator flux magnitude and torque in typical MPTC are converted into constant reference of stator flux vector in the proposed MPFC. As only the tracking error of stator flux vector is needed in the cost operate, the employment of weighting factor is eliminated. The optimal voltage vector is selected primarily based on the principle of stator flux error minimization and its switching instant is optimized instead of being in the start of each control period. The proposed MPFC with and without switching instant optimization are both implemented during a 32-bit floating digital signal processor, and they're compared very well in terms of torque ripple, current harmonics, and average switching frequency. Each digital simulations and experimental tests were dispensed on a two-level inverter-fed IM drive, and also the obtained results validate the effectiveness of the proposed technique.