1. Magnetic Field-Based Induction Machine Modeling Incorporating Space and Time Harmonic Effects
- Author
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Buddhika De Silva Guruwatta Vidanalage, Anthony Lombardi, Jimi Tjong, and Narayan C. Kar
- Subjects
Induction machine ,magnetomotive force drops ,minor flux density loops ,pulse width modulation ,space harmonics ,time harmonics ,Electrical engineering. Electronics. Nuclear engineering ,TK1-9971 - Abstract
Integration of the impacts of time and space harmonics simultaneously during the design stages of inverter-fed induction machines (IMs) is crucial for the accurate calculation of electromagnetic (EM) torque and ripples. Traditional magnetic field-based models offer an analytical approach for determining the EM torque in IMs by calculating inductances. However, by assuming infinite core permeabilities, these models neglect the impact of the core magnetomotive force (MMF) drops due to the difficulties in accurately calculating these drops and the impracticality of isolating the contribution from each phase, which is essential for inductances calculations. These factors contribute to deficiencies in this modeling approach, which become more noticeable when the combined impacts of time and space harmonics on MMF drop calculations are also disregarded. Therefore, this paper introduces a novel magnetic-field-based model to predict the torque and torque ripples of inverter-fed induction motors by addressing the above limitations. This involves modifying the turns and winding function, and calculating the core MMF drops based on the timely variation of non-sinusoidal core flux densities, considering their major and minor flux-density loop effects. Consequently, the associated energy is used to calculate the net available energy, thereby enhancing torque calculations. Compared to the experimental results obtained from an 11kW prototyped induction motor, the proposed model exhibits notable enhancements, achieving average accuracies of 96.4% for average torque and 94.51% for torque ripples, in contrast to the respective traditional model accuracies, 81.1% and 45.1%.
- Published
- 2024
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