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Cost optimization of submersible motors using a genetic algorithm and a finite element method

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Abstract

This paper presents an optimal design method to optimize cost of three-phase submersible motors. The optimally designed motor is compared with an industrial motor having the same ratings. The motor design procedure consists of a system of non-linear equations, which imposes induction motor characteristics, motor performance, magnetic stresses, and thermal limits. The genetic algorithm (GA) is used for cost optimization, and a software algorithm has been developed. As a result of the realized optimization, besides the improvements on the motor cost, motor torque improvements have also been acquired. The 2-D finite element method (FEM) is then used to confirm the validity of the optimal design. Computer simulation results are given to show the effectiveness of the proposed design process that can achieve a good prediction of the motor performance. Through the studies accomplished, it has been observed that submersible induction motors’ torques and efficiencies improve, their length reduces, and hence some material savings are obtained.

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Abbreviations

ACT:

Weighted average coil throw

A1m, Ab :

Cross-sectional area of stator and rotor conductor, respectively

Ar, Ag :

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

Cucost :

Cost of unit weight of copper

Cx :

Distribution factor

CSK :

Skewing constant

De :

Stator diameter at centers of stator slots

Do :

Stator outer diameter

Dr :

Rotor diameter

Fecost :

Cost of unit weight of iron

few :

End winding factor

g:

Air gap

Ib :

Rotor bar current

Kp1 :

Stator flux factor

Kwm :

Winding factor

Kzz :

Zig-zag leakage reactance constant

K1, K2 :

Carter coefficients

L1, L2 :

Axial length of stator and rotor, respectively

Pfe :

Density of the iron sheet

Psw, Prw :

Density of stator and rotor conductors, respectively

Pℓcu :

Total copper losses of stator and rotor

Pℓfe :

Total iron losses

qm :

Number of parallel paths in stator winding

rew :

Average length of end-winding

SF:

Stacking factor

St :

Saturation factor

S1, S2 :

Number of stator and rotor slot, respectively

wa, wr,:

Rotor end rings axial and radial width, respectively

ρm, ρb, ρr :

Resistivity of stator winding and rotor bar and end-ring, respectively

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Authors and Affiliations

  1. Technical Education Faculty, Electronics and Computer Education Department, Selcuk University, 42075, Konya, Turkey

    Mehmet Çunkaş

  2. Engineering and Architecture Faculty, Department of Electrical and Electronics Engineering, Selcuk University, 42075, Konya, Turkey

    Ramazan Akkaya & Osman Bilgin

Authors
  1. Mehmet Çunkaş
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  2. Ramazan Akkaya
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  3. Osman Bilgin
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Correspondence to Mehmet Çunkaş.

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Çunkaş, M., Akkaya, R. & Bilgin, O. Cost optimization of submersible motors using a genetic algorithm and a finite element method. Int J Adv Manuf Technol 33, 223–232 (2007). https://doi.org/10.1007/s00170-006-0458-x

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  • DOI: https://doi.org/10.1007/s00170-006-0458-x

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