Model-Based Predictive Rotor Field-Oriented Angle Compensation for Induction Machine Drives

Yang Liu, Jin Zhao and Quan Yin
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Yang Liu: School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
Jin Zhao: School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
Quan Yin: School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China

Energies, 2021, vol. 14, issue 8, 1-13

Abstract: In this paper, a model-based predictive rotor field-oriented angle compensation approach is proposed for induction machine drives. Indirect rotor field-oriented control is widely used in induction machine drives for its simple implementation and low cost. However, the accuracy of the rotor field-oriented angle is affected by variable parameters such as the rotor resistance and inductance. An inaccurate rotor field-oriented angle leads to a degradation of the torque and dynamic performance, especially in the high-speed flux-weakening region. Therefore, the d-axis and q-axis currents in the rotation reference frame are predicted based on the model and compared with the feedback current to correct the rotor field-oriented angle. To improve the stability and robustness, the proposed predictive algorithm is based on the storage current, voltage, and velocity data. The algorithm can be easily realized in real-time. Finally, the simulated and experimental results verify the algorithm’s effectiveness on a 7.5 kW induction machine setup.

Keywords: rotor field-oriented angle error; indirect rotor field-oriented control; induction machine drives; model-based prediction (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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