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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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Broken Bar Fault Detection in IM Operating Under No-Load Condition, RELJIC, D., JERKAN, D., MARCETIC, D., OROS, D.
Issue 4/2016



With new technologies, such as mobile communications, internet of things, and wide applications of social media, organizations generate a huge volume of data, much faster than several years ago. Big data, characterized by high volume, diversity and velocity, increasingly drives decision making and is changing the landscape of business intelligence, from governments to private organizations, from communities to individuals. Big data analytics that discover insights from evidences has a high demand for computing efficiency, knowledge discovery, problem solving, and event prediction. We dedicate a special section of Issue 4/2017 to Big Data. Prospective authors are asked to make the submissions for this section no later than the 31st of May 2017, placing "Big Data - " before the paper title in OpenConf.

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Thomson Reuters published the Journal Citations Report for 2015. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.459, and the JCR 5-Year Impact Factor is 0.442.

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  1/2015 - 10

Multi-objective Optimal Design of a Five-Phase Fault-Tolerant Axial Flux PM Motor

SAAVEDRA, H. See more information about SAAVEDRA, H. on SCOPUS See more information about SAAVEDRA, H. on IEEExplore See more information about SAAVEDRA, H. on Web of Science, RIBA, J.-R. See more information about  RIBA, J.-R. on SCOPUS See more information about  RIBA, J.-R. on SCOPUS See more information about RIBA, J.-R. on Web of Science, ROMERAL, L. See more information about ROMERAL, L. on SCOPUS See more information about ROMERAL, L. on SCOPUS See more information about ROMERAL, L. on Web of Science
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Download PDF pdficon (1,214 KB) | Citation | Downloads: 266 | Views: 1,203

Author keywords
motor design, fault-tolerance, optimization, permanent magnet machines, sizing equations

References keywords
magnet(20), permanent(17), motor(11), machines(9), design(9), optimization(8), flux(8), synchronous(7), axial(6), romeral(5)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2015-02-28
Volume 15, Issue 1, Year 2015, On page(s): 69 - 76
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2015.01010
Web of Science Accession Number: 000352158600010
SCOPUS ID: 84924803668

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Electric motors used for traction purposes in electric vehicles (EVs) must meet several requirements, including high efficiency, high power density and fault-tolerance. Among them, permanent magnet synchronous motors (PMSMs) highlight. Especially, five-phase axial flux permanent magnet (AFPM) synchronous motors are particularly suitable for in-wheel applications with enhanced fault-tolerant capabilities. This paper is devoted to optimally design an AFPM for in-wheel applications. The main geometric, electric and mechanical parameters of the designed AFPM are calculated by applying an iterative method based on a set of analytical equations, which is assisted by means of a reduced number of three-dimensional finite element method (3D-FEM) simulations to limit the computational burden. To optimally design the AFPM, a constrained multi-objective optimization process based on a genetic algorithm is applied, in which two objective functions are considered, i.e. the power density and the efficiency. Several fault-tolerance constraints are settled during the optimization process to ensure enhanced fault-tolerance in the resulting motor design. The accuracy of the best solution attained is validated by means of 3D-FEM simulations.

References | Cited By  «-- Click to see who has cited this paper

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References Weight

Web of Science® Citations for all references: 363 TCR
SCOPUS® Citations for all references: 523 TCR

Web of Science® Average Citations per reference: 11 ACR
SCOPUS® Average Citations per reference: 16 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references background updated on 2017-02-25 07:18 in 187 seconds.

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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania

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