Asset Details
Do you wish to reserve the book?
Performance driven multi objective optimization of 2 MW integrated Pseudo Direct Drive permanent magnet synchronous wind generator
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
Performance driven multi objective optimization of 2 MW integrated Pseudo Direct Drive permanent magnet synchronous wind generator
Do you wish to request the book?
Performance driven multi objective optimization of 2 MW integrated Pseudo Direct Drive permanent magnet synchronous wind generator
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
Performance driven multi objective optimization of 2 MW integrated Pseudo Direct Drive permanent magnet synchronous wind generator
2026
Overview
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.
Publisher
Nature Publishing Group UK,Nature Publishing Group,Nature Portfolio
Subject
