Abstract
Due to its numerous advantages, Direct Torque Control (DTC) method is the most extensively adopted technique in the industrial system. However, the ripple torque reduces the strategy's efficiency as a result of the employment of tree hysteresis comparative models, and the use of the PI speed regulator, and on the other hand the switching tables which generate variable switching frequencies. As a result, driving the machine at low speeds and, more precisely, altering motor resistance has an impact on the machine's behavior. As a result, this study provides a novel research technique for overcoming the machine's shortcomings in order to improve control performance. An intelligent DTC approach is applied to two Inverters that supplied the Doubly Fed Induction Motor (DFIM) by employing an Artificial Neuron Network (ANN). The motor and control behaviors were much improved using this technique, which was simulated in Matlab/Simulink.
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Mahfoud, S., Derouich, A., El Ouanjli, N. (2022). Performance Improvement of DTC for Doubly Fed Induction Motor by Using Artificial Neuron Network. In: Motahhir, S., Bossoufi, B. (eds) Digital Technologies and Applications. ICDTA 2022. Lecture Notes in Networks and Systems, vol 455. Springer, Cham. https://doi.org/10.1007/978-3-031-02447-4_4
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DOI: https://doi.org/10.1007/978-3-031-02447-4_4
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