Induction Motor Modeling Based on a Fuzzy Clustering Multi-Model—A Real-Time Validation
Affiliation(s)
National Engineering School of Gabes, Photovoltaic, Wind and Geothermal Systems Research, Gabes, Tunisia.
National Engineering School of Gabes, Photovoltaic, Wind and Geothermal Systems Research, Gabes, Tunisia.
ABSTRACT
This paper discusses a comparative study of two modeling methods based on multimodel approach. The first is based on C-means clustering algorithm and the second is based on K-means clustering algorithm. The two methods are experimentally applied to an induction motor. The multimodel modeling consists in representing the IM through a finite number of local models. This number of models has to be initially fixed, for which a subtractive clustering is necessary. Then both C-means and K-means clustering are exploited to determine the clusters. These clusters will be then exploited on the basis of structural and parametric identification to determine the local models that are combined, finally, to form the multimodel. The experimental study is based on MATLAB/SIMULINK environment and a DSpace scheme with DS1104 controller board. Experimental results approve that the multimodel based on K-means clustering algorithm is the most efficient.
This paper discusses a comparative study of two modeling methods based on multimodel approach. The first is based on C-means clustering algorithm and the second is based on K-means clustering algorithm. The two methods are experimentally applied to an induction motor. The multimodel modeling consists in representing the IM through a finite number of local models. This number of models has to be initially fixed, for which a subtractive clustering is necessary. Then both C-means and K-means clustering are exploited to determine the clusters. These clusters will be then exploited on the basis of structural and parametric identification to determine the local models that are combined, finally, to form the multimodel. The experimental study is based on MATLAB/SIMULINK environment and a DSpace scheme with DS1104 controller board. Experimental results approve that the multimodel based on K-means clustering algorithm is the most efficient.
KEYWORDS
Multi-Model Modeling, C-means Clustering Algorithm, K-Means Clustering Algorithm, Induction Motor (IM), Experimental Validation
Multi-Model Modeling, C-means Clustering Algorithm, K-Means Clustering Algorithm, Induction Motor (IM), Experimental Validation
Cite this paper
Aicha, A. , Mouna, B. and Lassaad, S. (2015) Induction Motor Modeling Based on a Fuzzy Clustering Multi-Model—A Real-Time Validation. International Journal of Modern Nonlinear Theory and Application, 4, 153-160. doi: 10.4236/ijmnta.2015.42011.
Aicha, A. , Mouna, B. and Lassaad, S. (2015) Induction Motor Modeling Based on a Fuzzy Clustering Multi-Model—A Real-Time Validation. International Journal of Modern Nonlinear Theory and Application, 4, 153-160. doi: 10.4236/ijmnta.2015.42011.
References
[1] Salahat, M., Barbarawe, O., Abu Zalata, M. and Asad, S. (2011) Modular Approach for Investigation of the Dynamic Behavior of Three-Phase Induction Machine at Load Variation. Engineering, 3, 525-531.
http://dx.doi.org/10.4236/eng.2011.35061 [2] Vodovozov, V., Gevorkov, L. and Raud, Z. (2014) Modeling and Analysis of Pumping Motor Drives in Hardware-in-the-Loop Environment. Journal of Power and Energy Engineering, 2, 19-27.
http://dx.doi.org/10.4236/jpee.2014.210003 [3] Men, Z.X., Yee, E., Lien, F.-S., Ji, H. and Liu, Y.Q. (2014) Bootstrapped Multi-Model Neural-Network Super-Ensembles for Wind Speed and Power Forecasting. Energy and Power Engineering, 6, 340-348.
http://dx.doi.org/10.4236/epe.2014.611029 [4] Shankar, H., Raju P.L.N. and Rao, K.R.M. (2012) Multi Model Criteria for the Estimation of Road Traffic Congestion from Traffic Flow Information Based on Fuzzy Logic. Journal of Transportation Technologies, 2, 50-62.
http://dx.doi.org/10.4236/jtts.2012.21006 [5] Abid, A., Ben Hamed, M. and Sbita, L. (2011) Induction Motor Real Time Application of Multimodel Modeling Approach. International Review of Electrical Engineering (IREE), 6, 655-660. [6] Abid, A., Adouni, A., Ben Hamed, M. and Sbita, L. (2012) A New PV Cell Model Based On Multi Model Approach. The 4th International Renewable Energy Congress, Sousse, 20-22 December 2012, 1447-1452. [7] Abid A., Ben Mabrouk Z., Ben Hamed M., Sbita L., (2013) Multiple Lunberger Observer for an Induction Motor Represented by Decoupled Multiple Model. 10th International Multi-Conference on Systems, Signals & Devices (SSD) Hammamet, March 18-21 2013, 1-6.
http://dx.doi.org/10.1109/ssd.2013.6564050 [8] Elfelly, N., Dieulot, J.-Y., Benrejeb, M. and Borne, P. (2012) A Multimodel Approach for Complex Systems Modeling Based on Classification Algorithms. International Journal of Computers Communications Control, 7, 644-659. [9] Orjuela, R., Marx, B., Ragot, J. and Maquin, D. (2013) Nonlinear System Identification Using Heterogeneous Multiple Models. International Journal of Applied Mathematics and Computer Science, 23, 103-115.
http://dx.doi.org/10.2478/amcs-2013-0009 [10] Hamdi, H., Rodrigues, M., Mechmeche, C., Theilliol, D. and BenHadj Braiek, N., (2012) Fault Detection and Isolation in Linear Parameter-Varying Descriptor Systems via Proportional Integral Observer. International Journal of Adaptive Control and Signal Processing, 26, 224-240.
http://dx.doi.org/10.1002/acs.1260 [11] Guldemir, H. and Sengur, A. (2006) Comparison of Clustering Algorithms for Analog Modulation Classification. Expert Systems with Applications, 30, 642-649.
http://dx.doi.org/10.1016/j.eswa.2005.07.014 [12] Velmurugan, T. (2014) Performance Based Analysis between K-Means and Fuzzy C-Means Clustering Algorithms for Connection Oriented Telecommunication Data. Applied Soft Computing, 19, 134-146.
http://dx.doi.org/10.1016/j.asoc.2014.02.011 [13] Chiu, S.L. (1994) Fuzzy Model Identification Based on Cluster Estimation. Journal of Intelligent and Fuzzy Systems, 2, 267-278. [14] Fan, J.L. and Li, J. (2014) A Fixed Suppressed Rate Selection Method for Suppressed Fuzzy C-Means Clustering Algorithm. Applied Mathematics, 5, 1275-1283.
http://dx.doi.org/10.4236/am.2014.58119
[1] Salahat, M., Barbarawe, O., Abu Zalata, M. and Asad, S. (2011) Modular Approach for Investigation of the Dynamic Behavior of Three-Phase Induction Machine at Load Variation. Engineering, 3, 525-531.
http://dx.doi.org/10.4236/eng.2011.35061 [2] Vodovozov, V., Gevorkov, L. and Raud, Z. (2014) Modeling and Analysis of Pumping Motor Drives in Hardware-in-the-Loop Environment. Journal of Power and Energy Engineering, 2, 19-27.
http://dx.doi.org/10.4236/jpee.2014.210003 [3] Men, Z.X., Yee, E., Lien, F.-S., Ji, H. and Liu, Y.Q. (2014) Bootstrapped Multi-Model Neural-Network Super-Ensembles for Wind Speed and Power Forecasting. Energy and Power Engineering, 6, 340-348.
http://dx.doi.org/10.4236/epe.2014.611029 [4] Shankar, H., Raju P.L.N. and Rao, K.R.M. (2012) Multi Model Criteria for the Estimation of Road Traffic Congestion from Traffic Flow Information Based on Fuzzy Logic. Journal of Transportation Technologies, 2, 50-62.
http://dx.doi.org/10.4236/jtts.2012.21006 [5] Abid, A., Ben Hamed, M. and Sbita, L. (2011) Induction Motor Real Time Application of Multimodel Modeling Approach. International Review of Electrical Engineering (IREE), 6, 655-660. [6] Abid, A., Adouni, A., Ben Hamed, M. and Sbita, L. (2012) A New PV Cell Model Based On Multi Model Approach. The 4th International Renewable Energy Congress, Sousse, 20-22 December 2012, 1447-1452. [7] Abid A., Ben Mabrouk Z., Ben Hamed M., Sbita L., (2013) Multiple Lunberger Observer for an Induction Motor Represented by Decoupled Multiple Model. 10th International Multi-Conference on Systems, Signals & Devices (SSD) Hammamet, March 18-21 2013, 1-6.
http://dx.doi.org/10.1109/ssd.2013.6564050 [8] Elfelly, N., Dieulot, J.-Y., Benrejeb, M. and Borne, P. (2012) A Multimodel Approach for Complex Systems Modeling Based on Classification Algorithms. International Journal of Computers Communications Control, 7, 644-659. [9] Orjuela, R., Marx, B., Ragot, J. and Maquin, D. (2013) Nonlinear System Identification Using Heterogeneous Multiple Models. International Journal of Applied Mathematics and Computer Science, 23, 103-115.
http://dx.doi.org/10.2478/amcs-2013-0009 [10] Hamdi, H., Rodrigues, M., Mechmeche, C., Theilliol, D. and BenHadj Braiek, N., (2012) Fault Detection and Isolation in Linear Parameter-Varying Descriptor Systems via Proportional Integral Observer. International Journal of Adaptive Control and Signal Processing, 26, 224-240.
http://dx.doi.org/10.1002/acs.1260 [11] Guldemir, H. and Sengur, A. (2006) Comparison of Clustering Algorithms for Analog Modulation Classification. Expert Systems with Applications, 30, 642-649.
http://dx.doi.org/10.1016/j.eswa.2005.07.014 [12] Velmurugan, T. (2014) Performance Based Analysis between K-Means and Fuzzy C-Means Clustering Algorithms for Connection Oriented Telecommunication Data. Applied Soft Computing, 19, 134-146.
http://dx.doi.org/10.1016/j.asoc.2014.02.011 [13] Chiu, S.L. (1994) Fuzzy Model Identification Based on Cluster Estimation. Journal of Intelligent and Fuzzy Systems, 2, 267-278. [14] Fan, J.L. and Li, J. (2014) A Fixed Suppressed Rate Selection Method for Suppressed Fuzzy C-Means Clustering Algorithm. Applied Mathematics, 5, 1275-1283.
http://dx.doi.org/10.4236/am.2014.58119