Artificial neural network speed estimator in a DTC scheme

Authors:
Pedro Ponce Cruz;Flavio Lucio Pontecorvo;José Ramón Álvarez Bada;Alfonso Monroy;Marco Paz
Affiliations:
Instituto Tecnológico y de Estudios Superiores de Monterrey, México D.F., México;Instituto Tecnológico y de Estudios Superiores de Monterrey, México D.F., México;Instituto Tecnológico y de Estudios Superiores de Monterrey, México D.F., México;Instituto Tecnológico y de Estudios Superiores de Monterrey, México D.F., México;Instituto Tecnológico y de Estudios Superiores de Monterrey, México D.F., México
Venue:
ACST'06 Proceedings of the 2nd IASTED international conference on Advances in computer science and technology
Year:
2006

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Artificial-Intelligence-Based Electrical Machines and Drives: Application of Fuzzy, Neural, Fuzzy-Neural, and Genetic-Algorithm-Based Techniques

Artificial-Intelligence-Based Electrical Machines and Drives: Application of Fuzzy, Neural, Fuzzy-Neural, and Genetic-Algorithm-Based Techniques

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Abstract

Direct torque control (DTC) is known to produce quick and robust response in AC drives. However, during steady state, torque, flux and current ripple occur. An improvement of the electric drive can be obtained using a DTC scheme based on the space vector modulation (SVM) which reduces the torque and flux ripple. The proposed control scheme considers the rotor resistance variation.This paper also discusses the application of artificial neural network (ANN) as a speed estimator.The capability and precision of this scheme as a speed estimator is verified by simulation results, from which it is concluded that the proposed control scheme produces better results than the classical DTC.