Parameters identification of 11-phase Torus generator using Particle Swarm Optimization technique

A. Al-Hinai; A. Al-Badi

doi:10.1109/PES.2008.4596691

Parameters identification of 11-phase Torus generator using Particle Swarm Optimization technique

A. Al-Hinai, A. Al-Badi

Electrical and Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Citations (Scopus)

Abstract

Axial-flux, Permanent-magnet machines are used in different applications where the power and torque density requirements are very high. These machines have simple structures, relatively high efficiencies and low cost. The parameters of these machines are very small compared with the parameters of conventional machines. Different measuring methods are normally used in order to obtain good estimates of the machine parameters. These methods are difficult to perform, costly and time consuming. This paper proposes the use of Particle Swarm Optimization (PSO) technique to predict the self and mutual inductances of the 11-phase Torus machine automatically.

Original language	English
Title of host publication	IEEE Power and Energy Society 2008 General Meeting: Conversion and Delivery of Electrical Energy in the 21st Century, PES
DOIs	https://doi.org/10.1109/PES.2008.4596691
Publication status	Published - 2008
Event	IEEE Power and Energy Society 2008 General Meeting: Conversion and Delivery of Electrical Energy in the 21st Century, PES - Pittsburgh, PA, United States Duration: Jul 20 2008 → Jul 24 2008

Other

Other	IEEE Power and Energy Society 2008 General Meeting: Conversion and Delivery of Electrical Energy in the 21st Century, PES
Country	United States
City	Pittsburgh, PA
Period	7/20/08 → 7/24/08

Keywords

Parameter identification
Particle Swarm Optimization technique
Torus machine

ASJC Scopus subject areas

Energy Engineering and Power Technology
Electrical and Electronic Engineering

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10.1109/PES.2008.4596691

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Cite this

Al-Hinai, A., & Al-Badi, A. (2008). Parameters identification of 11-phase Torus generator using Particle Swarm Optimization technique. In IEEE Power and Energy Society 2008 General Meeting: Conversion and Delivery of Electrical Energy in the 21st Century, PES [4596691] https://doi.org/10.1109/PES.2008.4596691

Al-Hinai, A & Al-Badi, A 2008, Parameters identification of 11-phase Torus generator using Particle Swarm Optimization technique. in IEEE Power and Energy Society 2008 General Meeting: Conversion and Delivery of Electrical Energy in the 21st Century, PES., 4596691, IEEE Power and Energy Society 2008 General Meeting: Conversion and Delivery of Electrical Energy in the 21st Century, PES, Pittsburgh, PA, United States, 7/20/08. https://doi.org/10.1109/PES.2008.4596691

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abstract = "Axial-flux, Permanent-magnet machines are used in different applications where the power and torque density requirements are very high. These machines have simple structures, relatively high efficiencies and low cost. The parameters of these machines are very small compared with the parameters of conventional machines. Different measuring methods are normally used in order to obtain good estimates of the machine parameters. These methods are difficult to perform, costly and time consuming. This paper proposes the use of Particle Swarm Optimization (PSO) technique to predict the self and mutual inductances of the 11-phase Torus machine automatically.",

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AB - Axial-flux, Permanent-magnet machines are used in different applications where the power and torque density requirements are very high. These machines have simple structures, relatively high efficiencies and low cost. The parameters of these machines are very small compared with the parameters of conventional machines. Different measuring methods are normally used in order to obtain good estimates of the machine parameters. These methods are difficult to perform, costly and time consuming. This paper proposes the use of Particle Swarm Optimization (PSO) technique to predict the self and mutual inductances of the 11-phase Torus machine automatically.

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