This is the second part of a paper, divided into two parts, dealing with the definition of a space-vector dynamic model of the linear Induction motor (LIM) taking into consideration both the dynamic end-effects and the iron losses as well as the off-line identification of its parameters. The first part has treated the theoretical framework of the model. This second part is devoted to the description of an identification technique which has been suitably developed for the estimation of the parameters of the LIM dynamic model accounting for both the dynamic end-effects and iron losses, described in the first part of the paper. Such an identification technique is strictly related to the state formulation of the proposed model and exploits Genetic Algorithms (GA) for minimizing a suitable cost function. The proposed dynamic model and its related parameters estimation technique have been validated comparing its results with those obtainable experimentally on a suitably developed test set-up as well as with those obtainable by a Finite Element Analysis (FEA) model of the LIM.

Accetta, A., Cirrincione, M., Pucci, M., Sferlazza, A. (2018). State Space-Vector Model of Linear Induction Motors Including Iron Losses: Part II: Model Identification and Results. In IEEE Energy Conversion Congress and Exposition (pp. 3190-3197). Institute of Electrical and Electronics Engineers Inc. [10.1109/ECCE.2018.8557544].

State Space-Vector Model of Linear Induction Motors Including Iron Losses: Part II: Model Identification and Results

Sferlazza, Antonino
2018-01-01

Abstract

This is the second part of a paper, divided into two parts, dealing with the definition of a space-vector dynamic model of the linear Induction motor (LIM) taking into consideration both the dynamic end-effects and the iron losses as well as the off-line identification of its parameters. The first part has treated the theoretical framework of the model. This second part is devoted to the description of an identification technique which has been suitably developed for the estimation of the parameters of the LIM dynamic model accounting for both the dynamic end-effects and iron losses, described in the first part of the paper. Such an identification technique is strictly related to the state formulation of the proposed model and exploits Genetic Algorithms (GA) for minimizing a suitable cost function. The proposed dynamic model and its related parameters estimation technique have been validated comparing its results with those obtainable experimentally on a suitably developed test set-up as well as with those obtainable by a Finite Element Analysis (FEA) model of the LIM.
2018
Settore ING-INF/04 - Automatica
9781479973125
Accetta, A., Cirrincione, M., Pucci, M., Sferlazza, A. (2018). State Space-Vector Model of Linear Induction Motors Including Iron Losses: Part II: Model Identification and Results. In IEEE Energy Conversion Congress and Exposition (pp. 3190-3197). Institute of Electrical and Electronics Engineers Inc. [10.1109/ECCE.2018.8557544].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10447/339732
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