This is the second part of an article, 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 offline identification of its parameters. This second part is devoted to the description of an identification technique that has been suitably developed for the estimation of the electrical parameters of the LIM dynamic model accounting for both the dynamic end-effects and iron losses. Such an identification technique is strictly related to the state formulation of the proposed model and exploits genetic algorithms for minimizing a suitable cost function based on the processing of both the primary current and speed estimation errors. The proposed parameters' estimation technique has been validated experimentally on a suitably developed test set-up. It has been further validated by a finite element analysis model of the LIM.

Accetta A., Cirrincione M., Pucci M., Sferlazza A. (2020). State-Space Vector Model of Linear Induction Motors including End-Effects and Iron Losses-Part II: Model Identification and Results. IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS, 56(1), 245-255 [10.1109/TIA.2019.2952034].

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

Sferlazza A.
2020-01-01

Abstract

This is the second part of an article, 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 offline identification of its parameters. This second part is devoted to the description of an identification technique that has been suitably developed for the estimation of the electrical parameters of the LIM dynamic model accounting for both the dynamic end-effects and iron losses. Such an identification technique is strictly related to the state formulation of the proposed model and exploits genetic algorithms for minimizing a suitable cost function based on the processing of both the primary current and speed estimation errors. The proposed parameters' estimation technique has been validated experimentally on a suitably developed test set-up. It has been further validated by a finite element analysis model of the LIM.
Settore ING-INF/04 - Automatica
https://ieeexplore.ieee.org/servlet/opac?punumber=28
Accetta A., Cirrincione M., Pucci M., Sferlazza A. (2020). State-Space Vector Model of Linear Induction Motors including End-Effects and Iron Losses-Part II: Model Identification and Results. IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS, 56(1), 245-255 [10.1109/TIA.2019.2952034].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10447/397600
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