Multiobjective Design Optimization of Stator for Synchronous Generator Using Bat Algorithm and Analysis of Magnetic Flux Density Distribution

Karaoğlan, Aslan Deniz; Perin, Deniz; Yilmaz, Kemal

doi:10.1080/15325008.2022.2049651

Cited by 1 publication

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“…Many researchers have studied magnetic device design optimization and permanent magnet synchronous generator (PMSG) design optimization, which are investigated in many research studies over the last few decades. Efficiency, magnetic flux density distribution, total harmonic distortion (THD), and other performance criteria are commonly used in these studies and are attempted to be improved [1][2][3][4][5][6][7][8][9][10][11]. The most common problems are heat buildup in the rotor, balancing issues, and bearing issues.…”

Section: Introductionmentioning

confidence: 99%

Design Optimization of 18-Poled High-Speed Permanent Magnet Synchronous Generator

Karaoğlan¹,

Perin²,

Yilmaz³

2023

Swarm Intelligence - Recent Advances and Current Applications

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The aim of this research is to optimize the design of an 18-poled 8000 rpm 7 kVA high-speed permanent magnet synchronous generator. The goal is to find the best factor levels for the design parameters, namely magnet thickness (MH), offset, and embrace (EMB) to optimize the responses namely efficiency (%), rated torque (N.m), air-gap flux density (Tesla), armature current density (A/mm2), armature thermal load (A2/mm3). The aim is to keep the air-gap flux density at 1 tesla while maximizing efficiency and minimizing the rest of the responses. Optimization was carried out with one sample algorithm selected from each of the commonly used optimization algorithm classifications. For this purpose, different class of well-known optimization techniques such as response surface methodology (gradient-based methods), genetic algorithm (evolutionary-based algorithms), particle swarm optimization algorithm (swarm-based optimization algorithms), and modified social group optimization algorithm (human-based optimization algorithms) are selected. In the Ansys Maxwell environment, numerical simulations are carried out. Mathematical modeling and optimizations are performed by using Minitab and Matlab, respectively. Confirmations are also performed. Results of the comparisons show that modified social group optimization and particle swarm optimization algorithms a bit outperform the response surface methodology and genetic algorithm, for this design problem.

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