A sensorless induction machine drive - An implementation study

Examensarbete för masterexamen

Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.12380/174192
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Type: Examensarbete för masterexamen
Master Thesis
Title: A sensorless induction machine drive - An implementation study
Authors: Carlsson, Håkan
Bergerlind, Johan
Abstract: This Master’s thesis describes a DSP implementation of the Statically Compensated Voltage Model (SCVM) algorithm for sensorless induction machine control. The algorithm is aimed to be implemented in a 16-bit fixed-point processor. In general, low speed operation with voltage model based control systems is problematic, especially when in generator mode. In generator mode with the load at nominal torque, the implemented design fails already at 7.5 Hz. With a lower load, 2 Nm or 54 % of nominal, the drive is able brake it to almost zero, but the speed accuracy is low. On the other hand, if the motor operates in motor mode, low speed is less problematic. Experiments show that this implementation can handle variations in stator resistance from -37 % up to +60 % with stable low speed operation and variations of at least ±60 % at nominal speed. Stable operation for all speeds is possible even during an increase of rotor resistance by 60 %, but the speed accuracy will be reduced by approximatly 2 %. The rotor resistance may decrease by 58 % at low speed and by 53 % at nominal speed before the drive destabilizes. The total leakage inductance can vary at least ±30 % without noticeably affecting the system. The thesis project is conducted in association with Aros electronics AB.
Keywords: Elkraftteknik;Lantbruksvetenskap, skogsbruk och fiske;Electric power engineering;Agricultural Science, Forestry and Fisheries
Issue Date: 2006
Publisher: Chalmers tekniska högskola / Institutionen för energi och miljö
Chalmers University of Technology / Department of Energy and Environment
URI: https://hdl.handle.net/20.500.12380/174192
Collection:Examensarbeten för masterexamen // Master Theses



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