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Multi-model modeling methods based on novel clustering strategy and comparative study: Application to induction machines
- Source :
- SSD
- Publication Year :
- 2015
- Publisher :
- IEEE, 2015.
-
Abstract
- This paper is a comparative study of three doubly fed induction motor (DFIM) speed modeling strategies through multi-model approach based on three clustering algorithms; subtractive, C-means and K-means clustering. The comparison leads to a novel clustering strategy compound of the three clustering algorithms. The novel clustering strategy is applied to modeling the speed of the doubly fed induction motor then validated experimentally on a 1kw induction motor. The experimental study is held with the help of MATLAB/SIMULINK and a dSpace system with DS1104 controller board based on digital signal processor (DSP) TMS320F240. Simulation and experimental results approve the efficiency of the proposed approach.
Details
- Database :
- OpenAIRE
- Journal :
- 2015 IEEE 12th International Multi-Conference on Systems, Signals & Devices (SSD15)
- Accession number :
- edsair.doi...........ff870a304b3e24349f4ad7f05aaac288