Research on Matching Characteristics of Wind Turbine and Generator for Small H-shaped Vertical Axis Wind Turbine
DOI:
https://doi.org/10.13052/dgaej2156-3306.3645Keywords:
Matching characteristics, CFD, wind turbine, generator.Abstract
Based on the small H-shaped vertical axis wind wheel model (NACA0016),
a CFD wind wheel model was constructed. Based on the principle of moving
grid, the grid division of the CFD wind wheel model is completed by using
GAMBIT software, and the boundary conditions such as the inlet boundary
and the outlet boundary are set reasonably. Then, the turbulence model and
the couple algorithm are used to carry out transient simulation calculations,
and finally the aerodynamic parameter curves of the two-dimensional CFD
wind wheel model are obtained. Based on this, the matching characteristics
of the wind turbine and generator of the small H-shaped vertical axis wind
turbine are studied. The research results show as follows: when the incoming
wind speeds change in range of (2 m/s, 12 m/s), and the power character-
istic curve and torque characteristic curve of the generator wind wheel are
respectively overlap the best power curve and best torque of the generator,
the matching characteristics of the small H-shaped vertical axis wind turbine
rotor and generator are optimal, which provides reference for carrying out
related research.
Downloads
References
Fei Rong, Xiaoyue Xu, Shijia Zhou et al. Optimized strategy for
DFIG wind farm considering turbine fatigue[J] International Journal of
Electrical Power and Energy Systems, 2020.
Wu Yun-ke, Li Zhi-qiang, Zhang Zhi-hong, Liu Li-fang. Numerical
Study on the Aerodynamics Design for MW-Level H-shaped Vertical
Axis Wind Turbine[J]. Journal of Engineering for Thermal Energy and
Power, 2019(6).
Jin Xin, Ju Wenbin, Ren Haijun, Yang Xiangang. Research of Aerody-
namic Analysis Method and Operation Law of H-Shaped Vertical Axis
Wind Turbine[J]. Acta Energiae Solaris Sinica, 2017(10).
Liu, Ran Hui. The Analysis of Power Performance for Small H-Vertical
Axis Wind Turbine, Advanced Materials Research, 2013.
Xing Zhi, Duan Xiangjun, Liu Lei. MPPT for wind power system with
switched reluctance generator, 2018 13th IEEE Conference on Industrial
Electronics and Applications (ICIEA), 2018.
Shi Jinyan, Xie Yongchao. Analysis of Performance of Small Verti-
cal Axis Wind Power Generator with NACA0016 Airfoil[J]. Science
Mosaic, 2015(8).
Xiaojing Sun, Wanli Zhou, Diangui Huang, Guoqing Wu. Preliminary
study on the matching characteristics between wind wheel and pump in
a wind-powered water pumping system[J]. Journal of Renewable and
Sustainable Energy, 2011.
Y.-C. Xie and J.-Y. Shi
M. Zheng, X. Zhang, L. Zhang, H. Teng, J. Hu, M. Hu. Uniform Test
Method Optimum Design for Drag-Type Modifified Savonius VAWTs
by CFD Numerical Simulation[J]. Arabian Journal for Science and
Engineering, 2017.
Lin Pan, Haodong Xiao, Yanwei Zhang, Zhaoyang Shi. Research on
Aerodynamic Performance of J-type Blade Vertical Axis Wind Turbine,
Chinese Control And Decision Conference (CCDC), 2020.
M. Ghasemian, Z.N. Ashrafi, A. Sedaghat. A review on computational
fluid dynamic simulation techniques for Darrieus vertical axis wind
turbines[J]. Energy Conversion & Management, 2017(1).
Bi Jihong, Wu Ji, Guan Jian, Wang Jian. Influence of Wind Speed,
Stay Cable Inclination Angle and Wind Yaw Angle on Formation of
Rivulets[J]. Transactions of Tianjin University, 2016(6).
Wu Chutian, Yang Xiaolei, Zhu Yaxin. On the design of potential turbine
positions for physics-informed optimization of wind farm layout[J].
Renewable Energy, 2021.
Kou Peng, Wang Chen, Liang Deliang et al. Deep learning approach
for wind speed forecasts at turbine locations in a wind farm[J]. IET
Renewable Power Generation, 2020.
Han Zhimin, Li Shenggang, Liu Heng. Composite learning sliding mode
synchronization of chaotic fractional-order neural networks[J]. Journal
of Advanced Research, 2020.
Bastankhah Majid, Welch Bridget L., Mart ́ınezTossas Luis A. et al.
Analytical solution for the cumulative wake of wind turbines in wind
farms[J]. Journal of Fluid Mechanics, 2021.
Ben Hoen, Jeremy Firestone, Joseph Rand et al. Attitudes of U.S. Wind
Turbine Neighbors: Analysis of a Nationwide Survey[J]. Energy Policy,
Sung-ho Hur. Modelling and control of a wind turbine and farm[J].
Energy, 2018.
D.H. Wood, V.L. Okulov, D. Bhattacharjee. Direct calculation of wind
turbine tip loss[J]. Renewable Energy, 2016.
Li Yanting, Wu Zhenyu. A condition monitoring approach of multi-
turbine based on VAR model at farm level[J]. Renewable Energy,
, 166.
Research on Matching Characteristics of Wind Turbine and Generator 439
Shaler Kelsey, Jonkman Jason Fast. Farm development and validation
of structural load prediction against large eddy simulations[J]. Wind
Energy, 2020.
Lu Kai Hung, Hong Chih Ming, Xu Qiangqiang. Recurrent wavelet-
based Elman neural network with modified gravitational search algo-
rithm control for integrated offshore wind and wave power generation
systems[J]. Energy, 2019.
