Improved Duty Ratio-Based Direct Torque Control for Open-End Winding Induction Motor Drives

This paper presents an improved duty-ratio-based direct torque control (Duty-DTC) scheme for open-end winding induction motor (OEWIM) drives. Unlike conventional DTC (CDTC), which applies a single voltage vector over the entire sampling interval, the proposed method adjusts the duty ratio of the active voltage vector based on the instantaneous torque error. A computationally efficient and robust algorithm is developed to determine the optimal duty ratio, achieving reductions in torque ripple, flux ripple, and switching frequency variations while preserving the transient response of CDTC. A torque reference compensation method is introduced to mitigate the steady-state torque error caused by variations in motor speed. A novel switching state optimization method is used, in which the dwell time of the zero-voltage vector is split into two equal intervals and the active voltage vector is placed at the center of the switching interval, further improving the steady-state error and ripples. The proposed Duty-DTC algorithm is verified experimentally under various operating conditions using a 5 kW OEWIM drive laboratory prototype. The experimental results show that the proposed algorithm achieves <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$2.41\%$</tex-math></inline-formula>, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$3.8\%$</tex-math></inline-formula>, and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$4.5\%$</tex-math></inline-formula> torque ripple at 60 RPM, 720 RPM, and 1440 RPM, respectively, demonstrating the effectiveness of the proposed Duty-DTC in reducing torque ripples. The comparative results demonstrate a 85% reduction in torque ripple without an increase in computational time and complexity compared to CDTC. The results also show that the proposed algorithm achieves performance comparable to that of artificial neural network-based Duty-DTC algorithms, but with reduced computational time and complexity.