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Improving Wind Turbine Blade Based on Multi-objective Particle Swarm Optimization

Lookup NU author(s): Dr Wenxian YangORCiD



This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND).


This paper studies a new method for optimizing the design of wind turbine blades. Compared with the existing blade design methods, the proposed method not only considers the structural strength and stiffness of the blade but also considers the noise and power generation performance of the blade. The method utilizes a multi-objective particle swarm optimization method and the finite volume method in combination to meet the strength and stiffness requirements of the wind turbine blade, improve its aerodynamic performance and reduce its noise. In the study, the geometries of the blade used by a 2 MW wind turbine are taken as the initial parameters of the target blade and MATLAB and ANSYS are employed to perform the optimization and finite element analysis based performance calculations. Then an intelligent optimization algorithm was developed for achieving a quiet and efficient wind turbine blade. In such a multi-objective optimization algorithm, both structural strength, stiffness, noise reduction, and aerodynamic performance of the blade are taken as objective functions. The simulation results have shown that through optimization, the blade noise was reduced by 3.1 dB and the power coefficient was increased by 6.9%. Moreover, it is found that the blade’s structural strength and stiffness are also improved after optimization. This implies that the proposed algorithm is also helpful to further reduce the manufacturing materials and costs of wind turbine blades.

Publication metadata

Author(s): Li Y, Wei K, Yang W, Wang Q

Publication type: Article

Publication status: Published

Journal: Renewable Energy

Year: 2020

Volume: 161

Pages: 525-542

Print publication date: 01/12/2020

Online publication date: 21/07/2020

Acceptance date: 13/07/2020

Date deposited: 13/07/2020

ISSN (print): 0960-1481

ISSN (electronic): 1879-0682

Publisher: Elsevier


DOI: 10.1016/j.renene.2020.07.067


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