Janisch, G., Kugi, A., & Kemmetmüller, W. (2022). Model calibration strategy for energy-efficient operation of induction machines. In A. Kugi, A. Körner, W. Kemmetmüller, A. Deutschmann-Olek, F. Breitenecker, & I. Troch (Eds.), 10th Vienna International Conference on Mathematical Modelling MATHMOD 2022: Vienna Austria, 27–29 July 2022 (pp. 307–312). https://doi.org/10.1016/j.ifacol.2022.09.113
10th Vienna International Conference on Mathematical Modelling MATHMOD 2022: Vienna Austria, 27–29 July 2022
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Volume:
55
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Date (published):
Sep-2022
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Event name:
10th Vienna International Conference on Mathematical Modelling MATHMOD 2022
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Event date:
27-Jul-2022 - 29-Jul-2022
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Event place:
Wien, Austria
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Number of Pages:
6
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Peer reviewed:
Yes
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Keywords:
induction machine; parameter identification; magnetic saturation; energy-efficient control; loss minimizing operation
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Abstract:
This contribution describes a novel calibration procedure for nonlinear induction machine models. It is based on measurements of the electrical quantities and the rotational speed, and uses additional measurements of the torque. Since the method uses quasi steady-state measurements, the calculations required for the model calibration can be performed with low computational costs in the frequency domain. Compared to the scientific state of the art, higher harmonics are taken into account in addition to the fundamental wave. The main goal of the parametrized model is to accurately capture the behavior of the induction machine in the vicinity of maximum efficiency operating points. Thus, the calibration strategy is tailored to these important operating points and allows to obtain a very high accuracy with a rather simple model. The proposed calibration procedure is verified by measurements on a test bench. It is shown that a high model accuracy can be achieved with the calibrated model.
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Research Areas:
Modeling and Simulation: 20% Automation and Robotics: 80%