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Design optimization of induction motors with differential evolution algorithms with an application in textile spinning

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F12%3A86084512" target="_blank" >RIV/61989100:27240/12:86084512 - isvavai.cz</a>

  • Result on the web

  • DOI - Digital Object Identifier

Alternative languages

  • Result language

    angličtina

  • Original language name

    Design optimization of induction motors with differential evolution algorithms with an application in textile spinning

  • Original language description

    This article deals with the design optimization of a squirrel-cage three-phase induction motor, selected as the driving power of spinning machines in the textile industry, using three newly developed versions of differential evolution (DE) algorithms called modified DE versions (CMDE, GMDE, and LMDE). Efficiency, which decides the operating or running cost of the motor (industry), is considered as the objective function. First, the algorithms are applied to design a general purpose motor with seven variables and nine performance-related parameters with their nominal values as constraints. To make the machine feasible, practically acceptable to serve in textile industries, and less costly to operate, certain constraints are modified in accordance with the demands of the spinning application. Comparison of the optimum designs with the industrial (existing) motor reveals that the motor designed by the proposed algorithms consumes less power input.

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)

  • CEP classification

    IN - Informatics

  • OECD FORD branch

Result continuities

  • Project

  • Continuities

    S - Specificky vyzkum na vysokych skolach

Others

  • Publication year

    2012

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Name of the periodical

    Applied Artificial Intelligence

  • ISSN

    0883-9514

  • e-ISSN

  • Volume of the periodical

    26

  • Issue of the periodical within the volume

    9

  • Country of publishing house

    US - UNITED STATES

  • Number of pages

    23

  • Pages from-to

    809-831

  • UT code for WoS article

    000311249500001

  • EID of the result in the Scopus database