Abstract
This work proposes an artificial neural network approach to estimate the induction motor speed applied to three-phase induction motor. The induction motor speed is the important variable in an industrial process. However, the direct measurement of speed compromises the driver system and control, besides increasing the implementation cost. The proposed strategy estimates the induction motor speed when it is driven by voltage source inverter with closed-loop scalar control. Simulation results are presented to validate the performance of the proposed method under motor load torque and speed reference point variations.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Vas, P.: Sensorless Vector and Direct Torque Control. Oxford University Press (1998)
Vasic, V., Vukosavic, S.N., Levi, E.: A stator resistance estimation scheme for speed sensorless rotor flux oriented induction motor drives. IEEE Transactions on Energy Conversion 18(4), 476–483 (2003)
Bose, B.K.: Modern Power Electronics and AC Drives. Prentice-Hall, New Jersey (2001)
Jevremovic, V.R., Vasic, V., Marcetic, D.P., Jeftenic, B.: Speed-sensorless control of induction motor based on reactive power with rotor time constant identification. Electric Power Applications, IET 4(6), 462–473 (2010)
Yoksel, O., Mehmet, D.: Speed estimation of vector controlled squirrel cage asynchronous motor with artificial neural networks. Energy and Manangement 52(1), 675–686 (2011)
Gadoue, S., Giaouris, D., Finch, J.: Sensorless control of induction motor drives at very low and zero speeds using neural network flux observers. IEEE Transactions on Industrial Electronics 56(8), 3029–3039 (2009)
Goedtel, A., Graciola, C., Silva, S., Nascimento, C., Suetake, M.: A comparative study for single and multilayer neural networks applied to speed estimation in induction motors. In: 2010 XIX International Conference on Electrical Machines (ICEM), pp. 1–6 (September 2010)
Ong, C.: Dynamic Simulation of Electric Machinery: Using Matlab/Simulink. Prentice-Hall, Upper Sanddle River (1998)
Krause, P.C., Wasynczuk, O., Sudhoff, S.D.: Analysis of Electric Machinery and Drives Systems. Piscataway, New Jersey (2002)
Goedtel, A., da Silva, I.N., Serni, P.J.A.: Load torque identification in induction motor using neural networks technique. Electric Power Systems Research 77(1), 35–45 (2007)
da Silva, S., Campanhol, L., Goedtel, A., Nascimento, C., Paiao, D.: A comparative analysis of p-pll algorithms for single-phase utility connected systems. In: 13th European Conference on Power Electronics and Applications, EPE 2009, pp. 1–10 (September 2009)
Haykin, S.: Neural network: a comprehensive foundation. Prentice Hall, New Jersey (1999)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
dos Santos, T.H., Goedtel, A., da Silva, S.A.O., Suetake, M. (2012). An ANN Speed Observer Applied to Three-Phase Induction Motor. In: Yin, H., Costa, J.A.F., Barreto, G. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2012. IDEAL 2012. Lecture Notes in Computer Science, vol 7435. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32639-4_13
Download citation
DOI: https://doi.org/10.1007/978-3-642-32639-4_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-32638-7
Online ISBN: 978-3-642-32639-4
eBook Packages: Computer ScienceComputer Science (R0)