GA-based Off-Line Parameter Estimation of the Induction Motor Model Including Magnetic Saturation and Iron Losses
Conference Paper
Publication Date:
2017
abstract:
This paper, starting from recent papers in the scientific literature dealing with Induction Motor (IM) dynamic modelling, as a first step, improves its space-vector dynamic model, including both the magnetic saturation and iron losses; particularly it takes into account the dependence of the magnetic saturation by the stator leakage inductance, as a further effect of the load. Afterwards, it proposes an off-line technique for the estimation of electrical parameters of this model, which is based on Genetic Algorithms (GA). The proposed method is based on input-output measurements and needs neither the machine design geometrical data nor a FEA of the machine. It focuses on the application of an algorithm based on the minimization of a suitable cost function depending on the stator current error. The proposed electrical parameters estimation method has been initially tested in numerical simulation and further verified experimentally on a suitably developed test set-up.
Iris type:
04.01 Contributo in Atti di convegno
Keywords:
Induction Motor (IM); Space-vector dynamic model; Magnetic saturation; Iron losses; Parameter Estimation; Identification
List of contributors:
Pucci, Marcello; Accetta, Angelo
Book title:
2017 IEEE Energy Conversion Congress and Exposition (ECCE)