This thesis analyses, develops and implements a very fast on-line parameter identification algorithm for both rotor and stator resistances of a rotor flux oriented induction motor drive, with the best possible convergence results using artificial neural networks and fuzzy logic systems. The thesis focuses mainly on identifying the rotor resistance, which is the most critical parameter for RFOC. Limitations of PI and fuzzy logic based estimators were identified. Artificial neural network based estimators were found to track the rotor and stator resistances of the drive accurately and fast. The rotor flux of the induction motor estimated with a classical voltage model was the key input of the rotor resistance estimator. Because, pure digital integrators were unable to play this role, an alternative rotor flux synthesizer using a programmable cascaded filter was developed. This rotor flux synthesizer has been used for all of the resistance estimators. It was found that the error in rotor resistance estimation using an ANN was contributed to by error in the stator resistance (caused by motor heating). Several stator resistance estimators using the stator current measurements were developed. The limitations of a PI and a fuzzy estimator for stator resistance estimation were also established. A new stator resistance identifier using an ANN was found to be much superior to the PI and fuzzy estimators, both in terms of dynamic estimation times and convergence problems. The rotor resistance estimator developed for this thesis used a feedforward neural network and the stator resistance estimator used a recurrent neural network. Both networks exhibited excellent learning capabilities; the stator resistance estimator network was very fast as it had a feedback input. A speed estimator was also developed with the state estimation principles, with the updated motor parameters supplied by the ANN estimators. Analysis for speed sensorless operation has shown that the stator and rotor resistances could be updated on-line.
| Identifer | oai:union.ndltd.org:ADTP/187965 |
| Date | January 2005 |
| Creators | Karanayil, Baburaj, Electrical Engineering & Telecommunications, Faculty of Engineering, UNSW |
| Publisher | Awarded by:University of New South Wales. Electrical Engineering and Telecommunications |
| Source Sets | Australiasian Digital Theses Program |
| Language | English |
| Detected Language | English |
| Rights | Copyright Baburaj Karanayil, http://unsworks.unsw.edu.au/copyright |
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