Electrical machines parameter identification using genetic algorithms
Kampisios, Konstantinos T. (2010) Electrical machines parameter identification using genetic algorithms. PhD thesis, University of Nottingham.
In Indirect Field Orientation (IFO) of induction motors, the interest for parameters identification has increased rapidly due to the great demand for high performance drives and more sophisticated control systems that have been made possible by the development of very powerful processors, such as floating point DSPs. Accurate knowledge of the machine electrical parameters is also required to ensure correct alignment of the stator current vector relative to the rotor flux vector, to decouple the flux - and torque - producing currents and to tune the current control loops. The accuracy and general robustness of the machine is dependant on this model.
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