Experimental Measurement Based Accurate Modelling Method for Switched Reluctance Motors

The switched reluctance motors (SRMs) are challenging for precise modeling, performance analyses, and sophisticated control because of their double saliency of structure, intense saturation of normal operation, and extreme nonlinearity of magnetization properties. This paper offers a precise techniq...

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Bibliographic Details
Published in:	2023 24th International Middle East Power System Conference (MEPCON) pp. 1 - 6
Main Authors:	Elkasas, Mahmoud I., Hamouda, Mahmoud, Ghanem, Abdelhady, Kotb, Mohamed F.
Format:	Conference Proceeding
Language:	English
Published:	IEEE 19-12-2023
Subjects:	Current measurement Data models electric machine modeling flux measurement magnetization characteristics Mathematical models Measurement uncertainty Power measurement Software packages switched reluctance motor (SRM) Switched reluctance motors torque measurement
Online Access:	Get full text
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Summary:	The switched reluctance motors (SRMs) are challenging for precise modeling, performance analyses, and sophisticated control because of their double saliency of structure, intense saturation of normal operation, and extreme nonlinearity of magnetization properties. This paper offers a precise technique for quantifying the SRM's flux-linkage and static torque properties, using a digital signal processor (DSP) and LabVIEW data acquisition system (DAQ) to execute the measurements. Realistic implementation and theoretical proof of the recommended technique are introduced and thoroughly explained. The measured data must be filtered to reduce the effect of signal errors and increase the accuracy of the generated model. The actual measurements of different operation values are used to validate the model accuracy and compare the measured data with the data resulting from the 6/4 SRM model of MATLAB/Simulink. Measurement accuracy is demonstrated by a comparison between simulated and experimentally observed current waveforms, which proves the high consistency of the generated model.
DOI:	10.1109/MEPCON58725.2023.10462424