Recurrent Neural Network Based Identification and Parameters Estimation of Induction Motor
A nonlinear autoregressive network with exogenous inputs (NARX) model of induction motor (IM) based on neural network (NN) is built. The developed artificial neural network (ANN) is trained based only on the inputs-outputs (I-O) data obtained from an elaborated simulation model with a good input design. Levenberg-Maquart (LM) and least square (LS) algorithms are used for the estimation of parameters, and this, for different network structures. The optimal model providing the best compromise between accuracy and complexity is selected based on the Akaike (AIC) criterion. The built model takes into consideration saturation and transient state. This model is also the most recommended for predictive control. Simulation results including model order selection and inputs design are shown and discussed in an automotive application.
Copyright © 2017 Praise Worthy Prize - All rights reserved.
A.K. Chattopadhyay, alternating current drives in the steel industry, IEEE Transsaction on Industrial Electronics Magazine, Vol. 4,(Issue 4), 30-42, December 2010.
I. Takahashi, T. Naguchi, A new quick response and high efficiency control strategy of an induction motor. IEEE Transsaction on industry applications. Vol. IA-22 (issue.5), 820– 827, September 1986.
J. Bocker and S. Mathapati, State of the art of induction motor control, IEEE International Electric Machines & Drives Conference, Vol. 2, pp.1459-1464, May 2007.
L. Zhang, R. Norman, and W. Shepherd, Long-range predictive control of current regulated PWM for induction motor drives using the synchronous reference frame, IEEE Transactions on Control Systems Technology, Vol. 5, 119–126, January. 1997.
F. Blaschke, The principle of field orientation applied to the new trans-vector closed-loop control system for rotating field machines, Siemens-Review 39, 217–220, 1972.
M. Depenbrock, Direct self control of inverter fed induction machines, IEEE Transaction on Power Electronics, Vol. 3 (Issue 4), 420-429 October 1988.
M. Wlas, Z. Krzemiński, H.A. Toliyat, Neural-Network-Based Parameter Estimations of Induction Motors, IEEE Transactions on Industrial Electronics, Vol. 55 (Issue 4), 1783-1794, April. 2010.
J.M. Gutierrez-Villalobos, J. Rodriguez-Resendiz, E.A. Rivas-Araiza, V.H. Mucino, A review of parameter estimators and controllers for induction motors based on artificial neural networks, Neurocomputing, Vol.118, October 2013.
S. Wade, M.W. Dunnigan, B.W. Williams, Modeling and simulation of induction machine vector control with rotor resistance identification, IEEE Transaction on Power Electronics, Vol. 12 (Issue 3) 495-506, May. 1997.
Benmiloud, T., Improved Adaptive Flux Observer of an Induction Motor with Fast Lyapunov Optimization Method, (2014) International Review of Electrical Engineering (IREE), 9 (2), pp. 300-306.
Sun, K., Shi, Y., Huang, L., Li, Y., An Improved Sensorless Control Method for IPMSM-Compressor Drives Based on the MRAS with Motor Parameter Variations, (2014) International Review of Electrical Engineering (IREE), 9 (1), pp. 73-82.
Mejia, W., Rodriguez, D., Rivera, S., Rosero Garcia, J., Heuristic Estimation of Parameters in High-Frequency Models of Induction Motors for Bearing Currents Simulation, (2016) International Review of Automatic Control (IREACO), 9 (6), pp. 355-364.
Moujahed, M., Ben Azza, H., Jemli, M., Boussak, M., Speed Estimation by Using EKF Techniques for Sensor-Less DTC of PMSM with Load Torque Observer, (2014) International Review of Electrical Engineering (IREE), 9 (2), pp. 270-279.
Jalil, N., Intan Z., M., Neuro Identification for Flexible Cantilever Beam Structure, (2014) International Review on Modelling and Simulations (IREMOS), 7 (2), pp. 341-349.
Picazo-Rodenas, M., Royo, R., Antonino-Daviu, J., A New Methodology for Complementary Diagnosis of Induction Motors Based on Infrared Thermography, (2015) International Journal on Energy Conversion (IRECON), 3 (2), pp. 44-52.
- There are currently no refbacks.
Please send any question about this web site to email@example.com
Copyright © 2005-2021 Praise Worthy Prize