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Intelligent design of induction motors by multiobjective fuzzy genetic algorithm

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Abstract

In this paper an approach using multi-objective fuzzy genetic algorithm (MFGA) for optimum design of induction motors is presented. Single-objective genetic algorithm optimization is compared with the MFGA optimization. The efficiency of those algorithms is investigated on motor’s performance. The comparison results show that MFGA is able to find more compromise solutions and is promising for providing the optimum design. Besides, a design tool is developed to evaluate and analysis the steady-state characteristics of induction motors.

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Abbreviations

A1m , A b :

Cross-sectional area of stator and rotor conductor, respectively

A r , A g :

Cross-sectional area of end-ring and air-gap, respectively

Cu cost :

Cost of unit weight of copper

D e :

Stator diameter at centers of stator slots

D o :

Stator outer diameter

D r :

Rotor diameter

Fe cost :

Cost of unit weight of iron

f ew :

End winding factor

L1, L2:

Axial length of stator and rotor, respectively

m :

Number of phase

p fe :

Density of the iron sheet

p sw , p rw :

Density of stator and rotor conductors, respectively

P ℓ cu :

Total copper losses of stator and rotor

P ℓ fe :

Total iron losses

s :

Slip

SF :

Stacking factor

S1, S2:

Number of stator and rotor slot, respectively

w a , w r :

Rotor end rings axial and radial width, respectively

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Authors and Affiliations

  1. Department of Electronics and Computer Education, Selçuk University, Konya, 42003, Turkey

    Mehmet Çunkaş

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Çunkaş, M. Intelligent design of induction motors by multiobjective fuzzy genetic algorithm. J Intell Manuf 21, 393–402 (2010). https://doi.org/10.1007/s10845-008-0187-0

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  • DOI: https://doi.org/10.1007/s10845-008-0187-0

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