GA-Based Off-Line Parameter Estimation of the Induction Motor Model Including Magnetic Saturation and Iron Losses

This paper, starting from recent papers in the scientific literature dealing with Rotating Induction Motor (RIM) dynamic modelling, as a first step, improves its space-vector dynamic model, including both the magnetic saturation and iron losses; The main original aspects of the proposed model are th...

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Veröffentlicht in:IEEE open journal of industry applications 2020, Vol.1, p.135-147
Hauptverfasser: Accetta, Angelo, Alonge, Francesco, Cirrincione, Maurizio, D'Ippolito, Filippo, Pucci, Marcello, Sferlazza, Antonino
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container_title IEEE open journal of industry applications
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creator Accetta, Angelo
Alonge, Francesco
Cirrincione, Maurizio
D'Ippolito, Filippo
Pucci, Marcello
Sferlazza, Antonino
description This paper, starting from recent papers in the scientific literature dealing with Rotating Induction Motor (RIM) dynamic modelling, as a first step, improves its space-vector dynamic model, including both the magnetic saturation and iron losses; The main original aspects of the proposed model are the following: 1) the magnetic saturation of the iron core has been described on the basis of both current versus flux and flux versus current functions, 3) it includes the iron losses, separating them in hysteresis and eddy current ones, 4) it includes the effect of the load on the magnetic saturation. 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 Finite Element Analysis (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.
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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 Finite Element Analysis (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. 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subjects Core loss
Cost function
Dynamic models
Eddy currents
Finite element method
Genetic algorithms
Identification
Induction motors
Iron
iron losses
Magnetic cores
Magnetic hysteresis
Magnetic induction
Magnetic saturation
Magnetic separation
Magnetism
Parameter estimation
rotating induction motor (rim)
Rotors
Saturation magnetization
space-vector dynamic model
Stators
title GA-Based Off-Line Parameter Estimation of the Induction Motor Model Including Magnetic Saturation and Iron Losses
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