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Direct torque control for induction machines using neural networks

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In this work, a novel switching vector selector in Direct Torque Control of an induction machine using Artificial Neural Network is studied. In the first part, we describe design of a speed sensor-less Direct Torque Control (DTC) strategy of an induction motor supplied by a two-level voltage source. For this, a conventional look up table is applied which improves the performances. Due to the high computation load, this technique is not convenient for an one-line and real-time control. Thus, a simplified method of choosing the output vector for two-level voltage source inverter-fed induction machine is proposed in the second part, and a novel switching vector selector using Artificial Neural Network (ANN) is trained under the tutor of the method mentioned above. The ANN receives attention as controllers for many industrial applications. Although these networks eliminate the need for mathematical models, they require a lot of training to understand the model of plant or process. In fact, when the stator flux and electromagnetic torque are different from theirs respective references, the output vector can be expediently acquired. Simulation results showed that the ANN structure can replace successfully the conventional look up table of the DTC.
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Bibliogr. 11 poz., rys., tab.
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