Multimodel Modeling of Doubly Fed Induction Motor
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As the electric drives are highly complex and nonlinear systems subject of several disturbances, it is a great problem to represent them via a unique model with sufficient precision and simple structure. Thus, this paper presents a new doubly fed induction motor (DFIM) model based on multimodel approach as a robust modeling method able to replace the complex system by a set of simpler local models. This approach consists of four steps which are clusters estimation, structure identification, parametric identification and local models combination. The collected data on DFIM are, firstly, clustered into several groups through a Chui’s clustering algorithm. Then, the structure identification is performed on each group using the instrumental ratio (RDI) method. Parameters of each sub model are identified using recursive least square (RLS) method. Finally, obtained sub models are combined using the validity concept. A detailed dynamic model of a DFIM with grid-connected stator and PWM inverter connected rotor, side is presented in the dq-synchronous reference frame in order to generate the input/output data. Simulation results are presented and analyzed up under Matlab/Simulink environment
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