Multi-objective optimization design of small wind turbine blade

被引：0
|
作者：
Xie Yongzhi [1 ,2 ]
Zhang Huan [2 ]
Qin Jianhua [2 ]
机构：
[1] Guangxi Key Lab New Energy & Bldg Energy Saving, Guilin 541004, Peoples R China
[2] Guilin Univ Technol, Coll Mech & Control Engn, Guilin 541004, Peoples R China
来源：
2019 4TH INTERNATIONAL CONFERENCE ON INTELLIGENT INFORMATION PROCESSING (ICIIP 2019) | 2019年
关键词：
blade; Lightweight; Wind energy capture; Optimized design; Fuzzy neural network;
D O I：
10.1145/3378065.3378121
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The multi - objective optimization design of small wind turbine blade is studied. Based on the theory of blade element - momentum and cantilever beam, combined with genetic algorithm, the mathematical model of optimal design of blade structure was established by taking the chord length, twist Angle and thickness of blade sections as design variables, the shape and stress as constraints, the lightweight of blade and the maximization of wind energy capture as objective functions. Taking a 10kW wind turbine blade as an example, the relationship between the three shape parameters of the blade before and after optimization and the impeller quality and wind energy capture of the wind turbine was analyzed. The calculation and analysis show that the optimized blade can not only improve the utilization coefficient of wind energy, but also reduce the mass line density of the blade section and realize the lightweight.
引用
收藏
页码：288 / 291
页数：4
相关论文
共 50 条
  • [1] Multi-objective Optimization of Ply Parameters for Wind Turbine Blade
    Dong, Xinhong
    Sun, Pengwen
    Zhang, Lanting
    Wang, Zongtao
    [J]. Jixie Gongcheng Xuebao/Journal of Mechanical Engineering, 2022, 58 (04): : 165 - 173
  • [2] Wind turbine blade geometry design based on multi-objective optimization using metaheuristics
    Vianna Neto, Julio Xavier
    Guerra Junior, Elci Jose
    Moreno, Sinvaldo Rodrigues
    Hultmann Ayala, Helon Vicente
    Mariani, Viviana Cocco
    Coelho, Leandro dos Santos
    [J]. ENERGY, 2018, 162 : 645 - 658
  • [3] Multi-objective optimization on blade airfoil of vertical axis wind turbine
    Zhang, Ruiyi
    Li, Deyou
    Chang, Hong
    Wei, Xuntong
    Wang, Hongjie
    [J]. PHYSICS OF FLUIDS, 2024, 36 (08)
  • [4] Multi-objective differential evolution optimization based on uniform decomposition for wind turbine blade design
    Wang, Long
    Wang, Tongguang
    Wu, Jianghai
    Chen, Guoping
    [J]. ENERGY, 2017, 120 : 346 - 361
  • [5] Optimization of Wind Turbine Blade Airfoils Using a Multi-Objective Genetic Algorithm
    Chen, Xiaomin
    Agarwal, Ramesh K.
    [J]. JOURNAL OF AIRCRAFT, 2013, 50 (02): : 519 - 527
  • [6] A hybrid multi-objective evolutionary algorithm for wind-turbine blade optimization
    Sessarego, M.
    Dixon, K. R.
    Rival, D. E.
    Wood, D. H.
    [J]. ENGINEERING OPTIMIZATION, 2015, 47 (08) : 1043 - 1062
  • [7] Improving wind turbine blade based on multi-objective particle swarm optimization
    Li, Yingjue
    Wei, Kexiang
    Yang, Wenxian
    Wang, Qiong
    [J]. RENEWABLE ENERGY, 2020, 161 (161) : 525 - 542
  • [8] Multi-objective deep reinforcement learning for optimal design of wind turbine blade
    Wang, Zheng
    Zeng, Tiansheng
    Chu, Xuening
    Xue, Deyi
    [J]. RENEWABLE ENERGY, 2023, 203 : 854 - 869
  • [9] Influence of Reynolds Number on Multi-Objective Aerodynamic Design of a Wind Turbine Blade
    Ge, Mingwei
    Fang, Le
    Tian, De
    [J]. PLOS ONE, 2015, 10 (11):
  • [10] Small Wind Turbine Blade Design and Optimization
    Muhsen, Hani
    Al-Kouz, Wael
    Khan, Waqar
    [J]. SYMMETRY-BASEL, 2020, 12 (01):
12345