Cascaded Neural Network for Internal Temperature Forecasting in Induction Motor
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Cascaded Neural Network for Internal Temperature Forecasting in Induction Motor

Authors: Hidir S. Nogay


In this study, two systems were created to predict interior temperature in induction motor. One of them consisted of a simple ANN model which has two layers, ten input parameters and one output parameter. The other one consisted of eight ANN models connected each other as cascaded. Cascaded ANN system has 17 inputs. Main reason of cascaded system being used in this study is to accomplish more accurate estimation by increasing inputs in the ANN system. Cascaded ANN system is compared with simple conventional ANN model to prove mentioned advantages. Dataset was obtained from experimental applications. Small part of the dataset was used to obtain more understandable graphs. Number of data is 329. 30% of the data was used for testing and validation. Test data and validation data were determined for each ANN model separately and reliability of each model was tested. As a result of this study, it has been understood that the cascaded ANN system produced more accurate estimates than conventional ANN model.

Keywords: Cascaded neural network, internal temperature, three-phase induction motor, inverter.

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[1] G. E. P. Box, G. Jenkins, Time Series Analysis, Forecasting and Control, Golden-Day, San Francisco, CA, 1970.
[2] T. M Hagan, H. B. Demuth, M. Beale, Neural Network Design, PWS Publishing Company, 1996, 2-44.
[3] H. S. Nogay, Y. Birbir, “Designation of Harmonic Estimation ANN Model Using Experimental Data Obtained From Different Produced Induction Motors”, 9th WSEAS Int. Conf. on Neural Networks (NN’ 08), Sofia, Bulgaria, May., 2 - 4, (2008).
[4] N. Amjady, F. Keynia, “Day-ahead price forecasting of electricity markets by a new feature selection algorithm and cascaded neural network technique”, Energy Conversion and
[5] D. H. Hwang, K. C. Lee, Y. J. Kim, Y. J. Lee, M. H. Kim, D. H. Kim, “Analysis of Voltage Distribution in Stator Winding of IGBT PWM Inverter-Fed Induction Motors”, IEEE ISIE 2005, June 20-23, Dubrovnik, Croatia, 2005.
[6] P. Gnacinski, “Effect of unbalanced voltage on windings temperature, operational life and load carrying capacity of induction machine”, Energy Conversion and Management, 49 (2008) 761–770.
[7] B. H. Ertan, M. Y. Uçtug, Modern Electrical Drives, Springer - Verlag, New York, USA, 2000
[8] S. Lee, G. Habetler, G. Harley and D. Gritter, “An Evaluation of Model-Based Stator Resistance Estimation for Induction Motor Stator Winding Temperature Monitoring”, IEEE Transactions on Energy Conversion, Vol. 17, No:1 March 2002
[9] F. Malik, M. Nasereddin, “Forecasting output using oil prices: A cascaded artificial neural network approach”, Journal of Economics and Business ,58 (2006) 168–180
[10] Jani J.T., M. I. Lehtokangas, J. P. P. Saarinen, “Evaluation of constructive neural networks with cascaded architectures”, Neurocomputing, 48 (2002) 573–607.