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http://dx.doi.org/10.5370/JEET.2010.5.4.597

Parameter Identification of Induction Motors using Variable-weighted Cost Function of Genetic Algorithms  

Megherbi, A.C. (Dept. of Electrical Engineering, Med Khider University)
Megherbi, H. (Dept. of Electrical Engineering, Med Khider University)
Benmahamed, K. (Dept. of Electronic Engineering, Ferhat Abass University)
Aissaoui, A.G. (Dept. of Electrical Engineering, Bechar University)
Tahour, A. (Dept. of Electrical Engineering, Bechar University)
Publication Information
Journal of Electrical Engineering and Technology / v.5, no.4, 2010 , pp. 597-605 More about this Journal
Abstract
This paper presents a contribution to parameter identification of a non-linear system using a new strategy to improve the genetic algorithm (GA) method. Since cost function plays an important role in GA-based parameter identification, we propose to improve the simple version of GA, where weights of the cost function are not taken as constant values, but varying along the procedure of parameter identification. This modified version of GA is applied to the induction motor (IM) as an example of nonlinear system. The GA cost function is the weighted sum of stator current and rotor speed errors between the plant and the model of induction motor. Simulation results show that the identification method based on improved GA is feasible and gives high precision.
Keywords
Genetic algorithm; Induction motor; Parameter identification; Cost function;
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