Predictive Torque Control with Online Weighting Factor Computation Technique to Improve Performance of Induction Motor Drive in Low Speed Region

Abstract

This paper presents a predictive torque control (PTC) method with online weighting factor computation based on the principle of coefficient of variation (CV) while maintaining the steady state, dynamic, and low-speed performance of three-phase induction motor (IM) drive. In PTC, appropriate weighting factor values are essential for the satisfactory performance of the drive. The main challenge is the tedious tuning of weighting factors, which requires rigorous simulation to obtain the desired performance of the drive. In this paper, a method is proposed that estimates online weighting factors, which modifies the cost function consecutively based on the principle of CV. In this method, the ranking of constraint is based on its magnitude of dispersion, whereas in other tuning methods, ranks are assigned beforehand. The proposed method (CV-PTC) is compared with the conventional normalized weighting factor-based PTC method specifically to improve the demagnetization effect in the low-speed operation. In the CV-PTC algorithm, synthetic voltage vectors are implemented to improve the performance of IM in full operating speed range. The experimental analysis is carried out on the IM drive prototype to validate the results and showcase the independence imparted for the selection of weights in the cost function.