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<p>Magnetic gearboxes (MGs) are increasingly explored in wind turbines as a reliable alternative to mechanical gearboxes, which suffer failures and costly maintenance. By integrating MG with Permanent Magnet Synchronous Generator, an Integrated Pseudo-Direct-Drive PMSG (IPDD–PMSG) is achieved, offering higher efficiency, contact-free operation, and improved system reliability. The proposed design <b>improved the performance of the IPDD–PMSG systems in torque and efficiency for 2&#xa0;MW wind turbines</b>. A multi-objective optimization is implemented to achieve maximum volumetric torque density (VTD) as well as minimum cost. Both Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) heuristics methods of optimization are investigated in order to reach the best algorithm. Results showed that GA offers better convergence and higher-performing solutions, while PSO ensures a greater diversity of cost/VTD trade-offs. Thus, the proposed methodology enables optimized and balanced design systems and offers a significant improvement in VTD and cost <b>of the system. The finite element approach verifies the geometry using the variables found by optimization.</b></p>

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Performance driven multi objective optimization of 2 MW integrated Pseudo Direct Drive permanent magnet synchronous wind generator

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月21日

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  • Dorra Abdeljalil,
  • Manel Krichen,
  • Naila Benhalima,
  • Naourez Benhadj,
  • Rafik Neji

摘要

Magnetic gearboxes (MGs) are increasingly explored in wind turbines as a reliable alternative to mechanical gearboxes, which suffer failures and costly maintenance. By integrating MG with Permanent Magnet Synchronous Generator, an Integrated Pseudo-Direct-Drive PMSG (IPDD–PMSG) is achieved, offering higher efficiency, contact-free operation, and improved system reliability. The proposed design improved the performance of the IPDD–PMSG systems in torque and efficiency for 2 MW wind turbines. A multi-objective optimization is implemented to achieve maximum volumetric torque density (VTD) as well as minimum cost. Both Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) heuristics methods of optimization are investigated in order to reach the best algorithm. Results showed that GA offers better convergence and higher-performing solutions, while PSO ensures a greater diversity of cost/VTD trade-offs. Thus, the proposed methodology enables optimized and balanced design systems and offers a significant improvement in VTD and cost of the system. The finite element approach verifies the geometry using the variables found by optimization.