Induction Motor Modeling Based on a Fuzzy Clustering Multi-Model—A Real-Time Validation

Abid  Aicha; Bnhamed  Mouna; Sbita  Lassaad

doi:10.4236/ijmnta.2015.42011

Abid Aicha^*, Bnhamed Mouna, Sbita Lassaad

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.

Keywords: 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.

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