Identification of asynchronous machines

Rotating electrical machines play a very important role in the world?s industry. Theasynchronous (induction) motor itself is a very superior electric motor, being simple androbust in structure, easy to maintain and very reliable. The three phase squirral cageinduction motor is used in this work. Nowadays, with the advantages in digital signalprosessor technology, control techniques for induction motor can now satisfactory beimplemented for real time applications.System Identification involves collecting normal operating input-output data and thencomputing the system parameters by some technique. Measured data are used to determinethe system behaviour within a certain class of mathematical models. Parameteridentification is critical for high performance control strategies in which both dynamicperformance and energy efficiency are expected.In this thesis, ARX (Autoregressive model with eXogenous inputs) model, is presented todescribe a modelling between the speed and voltages of an induction motor. This paperdeals with experimental on-line identification of three phase induction motors by means ofrecursive least square techniques. Studies are carried out by formulating the mathematicalmodel using differential equations and experimental discrete-time identification using on-line plant input-output data. A Recursive Least-Square Method (RLS) is used to estimatethe unknown parameters of the system.Key Words: System Identification, Modeling of Asynchronous Motor, Recursive LeastSquare Method