Integrated Optimal Design of Permanent Magnet Synchronous Generator for Smart Wind Turbine Using Genetic Algorithm
Henda Zorgani Agrebi,
Naourez Benhadj,
Mohamed Chaieb,
Farooq Sher,
Roua Amami,
Rafik Neji and
Neil Mansfield
Additional contact information
Henda Zorgani Agrebi: Department of Electrical Engineering, National School of Engineering of Gabes, University of Gabes, Gabes 6029, Tunisia
Naourez Benhadj: Department of Electrical Engineering, National School of Engineering of Sfax, University of Sfax, Soukra Sfax 3036, Tunisia
Mohamed Chaieb: Department of Electrical Engineering, National School of Engineering of Carthage, University of Tunis, Tunis 2035, Tunisia
Farooq Sher: Department of Engineering, School of Science and Technology, Nottingham Trent University, Nottingham NG11 8NS, UK
Roua Amami: Higher Institute of Agricultural Sciences, University of Sousse, Chott Meriem 4042, Tunisia
Rafik Neji: Department of Electrical Engineering, National School of Engineering of Sfax, University of Sfax, Soukra Sfax 3036, Tunisia
Neil Mansfield: Department of Engineering, School of Science and Technology, Nottingham Trent University, Nottingham NG11 8NS, UK
Energies, 2021, vol. 14, issue 15, 1-20
Abstract:
In recent years, the investment in the wind energy sector has increased in the context of producing green electricity and saving the environment. The installation of small wind turbines (SWTs) represents an actual strategy for meeting energy needs for off-grid systems and certain specialized applications. SWTs are more expensive per kilowatt installed as compared to large-scale wind turbines. Therefore, the main objective of this study is to produce an economical technology for the wind power market offering low-cost SWTs. The idea consists of considering a simple structure of the wind turbine using direct-drive permanent magnet synchronous generator (DDPMSG). DDPMSGs are the most useful machines in the wind energy field thanks to several advantages, such as elimination of noise and maintenance cost due to suppression of the gearbox and absence of the rotor circuit excitation barriers by the presence of the permanent magnets (PMs). Their major downside is the high cost of active materials, especially the PMs. Thus, the improvement of the generator design is treated as being the main component of the considered chain to assure active materials’ mass and cost reduction. The methodology studied aims to explain the approach of the design integrated by optimization of the considered system. It is based on the elaboration of analytical models to find a feasible structure for the system, taking into account the multi-disciplinary analysis. The relevance of these models is validated by the finite element method using 2D MATLAB-FEMM simulation. The models are integrated to elaborate the optimization problem based on a genetic algorithm to improve the cost of the proposed generator by minimizing the mass of its active constructive materials. As an outcome, an optimal solution is offered for the wind generators market, providing a 16% cost reduction.
Keywords: renewable energy; small wind turbine; wind energy; direct drive permanent magnet synchronous generator; finite element analysis; optimization and genetic algorithm (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: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)
Downloads: (external link)
https://www.mdpi.com/1996-1073/14/15/4642/pdf (application/pdf)
https://www.mdpi.com/1996-1073/14/15/4642/ (text/html)
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:14:y:2021:i:15:p:4642-:d:605718
Access Statistics for this article
Energies is currently edited by Ms. Agatha Cao
More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().