Improving wind turbine blade based on multi-objective particle swarm optimization

Yingjue Li, Kexiang Wei, Wenxian Yang and Qiong Wang

Renewable Energy, 2020, vol. 161, issue C, 525-542

Abstract: 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 efficiency 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.

Keywords: Wind turbine blade; Noise reduction; Optimization design; Multi-objective particle swarm optimization (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:161:y:2020:i:c:p:525-542

DOI: 10.1016/j.renene.2020.07.067

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