Enhanced loss model algorithm for interior permanent magnet synchronous machines

Rosario Miceli; Massimo Caruso; Claudio Nevoloso; Antonino Oscar Di Tommaso; Fabio Viola; Ciro Spataro

Enhanced loss model algorithm for interior permanent magnet synchronous machines

Rosario Miceli, Massimo Caruso, Claudio Nevoloso, Antonino Oscar Di Tommaso, Fabio Viola, Ciro Spataro

Risultato della ricerca: Conference contribution

Abstract

This paper presents an experimental study on the impact of the parameter variations over the performances of a LMA (Loss Model Algorithm) designed for an IPMSM (Interior Permanent Magnet Synchronous Machine). In a previous work, the characterization was carried out by assessing, for several working conditions, the motor parameters that influence the motor efficiency. The proposed enhanced loss model algorithm is implemented in a rapid prototyping system and its performances, in term of efficiency, are compared with other control systems, obtaining promising results.

Lingua originale	English
Titolo della pubblicazione ospite	2017 AEIT International Annual Conference: Infrastructures for Energy and ICT: Opportunities for Fostering Innovation, AEIT 2017
Pagine	1-6
Numero di pagine	6
Stato di pubblicazione	Published - 2017

All Science Journal Classification (ASJC) codes

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title = "Enhanced loss model algorithm for interior permanent magnet synchronous machines",

abstract = "This paper presents an experimental study on the impact of the parameter variations over the performances of a LMA (Loss Model Algorithm) designed for an IPMSM (Interior Permanent Magnet Synchronous Machine). In a previous work, the characterization was carried out by assessing, for several working conditions, the motor parameters that influence the motor efficiency. The proposed enhanced loss model algorithm is implemented in a rapid prototyping system and its performances, in term of efficiency, are compared with other control systems, obtaining promising results.",

author = "Rosario Miceli and Massimo Caruso and Claudio Nevoloso and {Di Tommaso}, {Antonino Oscar} and Fabio Viola and Ciro Spataro",

year = "2017",

language = "English",

isbn = "978-8-8872-3737-5",

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booktitle = "2017 AEIT International Annual Conference: Infrastructures for Energy and ICT: Opportunities for Fostering Innovation, AEIT 2017",

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TY - GEN

T1 - Enhanced loss model algorithm for interior permanent magnet synchronous machines

AU - Miceli, Rosario

AU - Caruso, Massimo

AU - Nevoloso, Claudio

AU - Di Tommaso, Antonino Oscar

AU - Viola, Fabio

AU - Spataro, Ciro

PY - 2017

Y1 - 2017

N2 - This paper presents an experimental study on the impact of the parameter variations over the performances of a LMA (Loss Model Algorithm) designed for an IPMSM (Interior Permanent Magnet Synchronous Machine). In a previous work, the characterization was carried out by assessing, for several working conditions, the motor parameters that influence the motor efficiency. The proposed enhanced loss model algorithm is implemented in a rapid prototyping system and its performances, in term of efficiency, are compared with other control systems, obtaining promising results.

AB - This paper presents an experimental study on the impact of the parameter variations over the performances of a LMA (Loss Model Algorithm) designed for an IPMSM (Interior Permanent Magnet Synchronous Machine). In a previous work, the characterization was carried out by assessing, for several working conditions, the motor parameters that influence the motor efficiency. The proposed enhanced loss model algorithm is implemented in a rapid prototyping system and its performances, in term of efficiency, are compared with other control systems, obtaining promising results.

UR - http://hdl.handle.net/10447/420379

M3 - Conference contribution

SN - 978-8-8872-3737-5

SP - 1

EP - 6

BT - 2017 AEIT International Annual Conference: Infrastructures for Energy and ICT: Opportunities for Fostering Innovation, AEIT 2017

ER -

Enhanced loss model algorithm for interior permanent magnet synchronous machines

Abstract

All Science Journal Classification (ASJC) codes

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