Investigation on Optimization Design of Offshore Wind Turbine Blades based on Particle Swarm Optimization

Yong Ma, Aiming Zhang, Lele Yang, Chao Hu and Yue Bai
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Yong Ma: School of Marine Engineering and Technology, Sun Yat-sen University, Guangzhou 518000, China
Aiming Zhang: School of Marine Engineering and Technology, Sun Yat-sen University, Guangzhou 518000, China
Lele Yang: School of Marine Engineering and Technology, Sun Yat-sen University, Guangzhou 518000, China
Chao Hu: College of Shipbuilding Engineering, Harbin Engineering University, Harbin 150001, China
Yue Bai: College of Shipbuilding Engineering, Harbin Engineering University, Harbin 150001, China

Energies, 2019, vol. 12, issue 10, 1-18

Abstract: Offshore wind power has become an important trend in global renewable energy development. Based on a particle swarm optimization (PSO) algorithm and FAST program, a time-domain coupled calculation model for a floating wind turbine is established, and a combined optimization design method for the wind turbine’s blade is developed in this paper. The influence of waves on the power of the floating wind turbine is studied in this paper. The results show that, with the increase of wave height, the power fluctuation of the wind turbine increases and the average power of the wind turbine decreases. With the increase of wave period, the power oscillation amplitude of the wind turbine increases, and the power of the wind turbine at equilibrium position decreases. The optimal design of the offshore floating wind turbine blade under different wind speeds is carried out. The results show that the optimum effect of the blades is more obvious at low and mid-low wind speeds than at rated wind speeds. Considering the actual wind direction distribution in the sea area, the maximum power of the wind turbine can be increased by 3.8% after weighted optimization, and the chord length and the twist angle of the blade are reduced.

Keywords: wind turbine; PSO algorithm; FAST; time-domain coupled model; blade optimization (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: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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