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E. Montero, M Vogelsberger, W. Teppan, J. Ertl, T. Wolbank:
"Online Signal Processing for accurate slotting saliency extraction using two-active SVPWM integrated excitation for sensorless induction machine control";
Talk: Intelligent Motion, Renewable Energy and Energy Management, Nürnberg; 2020-07-07 - 2020-07-08; in: "PCIM 2020", (2020), 6 pages.

Sensorless motor control approaches that use voltage step excitation rely on machine anisotropy extraction. Given the six-sector switching characteristic of SVPWM, it is sufficient to use one or two active inverter states to form a saliency-offset vector. Using two active states leads to more prominent saliency amplitudes related to the signal noise compared to such signals using only a single active-state. This is due to the non-deterministic characteristic of noise. For extracting saliency information from the saliencyoffset vector using two active states, each sector offset is required to be accurately estimated during online operation. This paper, therefore, presents an online strategy based on a SVPWM-integrated two-active excitation that estimates all sector offsets, and shows outstanding sensorless operation, validated by measurements taken at an induction motor.