The induction motor parameters of a traction vehicle can vary within a wide range as a result of heating, saturation and skin effect. The parameter mismatch may bring about some considerable control errors. To correct them one can use some estimators of most error-causing parameters. A traditional approach to this problem yields a very complicated set of equations. To obtain some satisfactory results at real-time control, it is necessary to carry out a great number of computations, which involve many processors operating paralelly. In this article the authors propose two parameter estimation methods, which combine both accuracy and speed computations in a wide range of velocity and load. The one is based on neural network, and the other utilises genetic algorithms for the identification. Their performances were simulated and discussed for direct torque and flux control method.
Asynchronous motor parameters estimation in traction vehicle
Parameterabschätzung an Asynchronmotoren für Elektrofahrzeuge
1997
6 Seiten, 7 Bilder, 1 Tabelle, 15 Quellen
Aufsatz (Konferenz)
Englisch
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