Electrical Machine Winding Performance Optimization by Multi-Objective Particle Swarm Algorithm

François S. Martins (), Bernardo P. Alvarenga and Geyverson T. Paula
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François S. Martins: School of Electrical, Mechanical and Computing Engineering, Federal University of Goiás, Av. Universitária, 1488, Goiânia 74605-010, Brazil
Bernardo P. Alvarenga: School of Electrical, Mechanical and Computing Engineering, Federal University of Goiás, Av. Universitária, 1488, Goiânia 74605-010, Brazil
Geyverson T. Paula: School of Electrical, Mechanical and Computing Engineering, Federal University of Goiás, Av. Universitária, 1488, Goiânia 74605-010, Brazil

Energies, 2024, vol. 17, issue 10, 1-19

Abstract: The present work aims to optimize the magnetomotive force and the end-winding leakage inductance from a discrete distribution of conductors in electrical machines through multi-objective particle swarm heuristics. From the development of an application capable of generating the conductor distribution for different machine configurations (single or poly-phase, single or double layer, integral or fractional slots, full or shortened pitch, with the presence of empty slots, etc.) the curves of magnetomotive force and the end-winding leakage inductance associated with the winding are computed. Taking as an optimal winding the one that presents, simultaneously, less harmonic distortion of the magnetomotive force and less leakage inductance, optimization by multi-objective particle swarm was used to obtain the optimal electrical machine configuration and the results are presented.

Keywords: magnetomotive force; winding optimization; particle swarm; multi-objective optimization (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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