This paper describes a novel Direct Torque Control (DTC) method for adjustable speed Doubly-Fed Induction Machine (DFIM) drives which is supplied by a two-level Space Vector Modulation (SVM) voltage source inverter (DTC-SVM) in the rotor circuit. The inverter reference voltage vector is obtained by using input-output feedback linearization control and a DFIM model in the stator a-b axes reference frame with stator currents and rotor fluxes as state variables. Moreover, to make this nonlinear controller stable and robust to most varying electrical parameter uncertainties, a two layer recurrent Artificial Neural Network (ANN) is used to estimate a certain function which shows the machine lumped uncertainty. The overall system stability is proved by the Lyapunov theorem. It is shown that the torque and flux tracking errors as well as the updated weights of the ANN are uniformly ultimately bounded. Finally, effectiveness of the proposed control approach is shown by computer simulation results.
In this study a steady-state equivalent circuit for a brushless doubly fed machine (BDFM), based upon earlier work, is introduced which takes account of core losses. Based upon some loss approximations, simple relationships have been derived, which show that the synchronous mode operation, in terms of core loss, of the BDFM is similar to the cascaded doubly fed machine. The slip-dependence of core loss resistances in the equivalent circuit has been investigated by applying energy conservation to derive machine steady-state torque-speed relations in the presence of core loss. Experimental speed measurements of the BDFM in the simple induction mode were used to identify the core loss resistance values.
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