基于反向传播神经网络的风力机涡流发生器优化

doi:10.16183/j.cnki.jsjtu.2022.169

上海交通大学学报 ›› 2023, Vol. 57 ›› Issue (11): 1492-1500.doi: 10.16183/j.cnki.jsjtu.2022.169

所属专题：《上海交通大学学报》2023年“新型电力系统与综合能源”专题

• 新型电力系统与综合能源 • 上一篇下一篇

基于反向传播神经网络的风力机涡流发生器优化

夏云松, 谭剑锋(), 韩水, 高金娥

南京工业大学机械与动力工程学院, 南京 211816

收稿日期:2022-05-20 修回日期:2022-06-17 接受日期:2022-07-13 出版日期:2023-11-28 发布日期:2023-12-01
通讯作者: 谭剑锋,副教授;E-mail: Jianfengtan@njtech.edu.cn.
作者简介:夏云松(1994-),研究生,从事风力机流动控制研究.
基金资助:
国家自然科学基金(12172165);江苏省自然科学基金(BK20211259)

Optimization of Wind Turbine Vortex Generator Based on Back Propagation Neural Network

XIA Yunsong, TAN Jianfeng(), HAN Shui, GAO Jin’e

School of Mechanical and Power Engineering, Nanjing Tech University, Nanjing 211816, China

Received:2022-05-20 Revised:2022-06-17 Accepted:2022-07-13 Online:2023-11-28 Published:2023-12-01

摘要/Abstract

摘要：

采用最优拉丁超立方试验设计法细化涡流发生器参数,确定试验方案,仿真计算风力机的推力和转矩,获得试验数据.基于反向传播(BP)神经网络,构建遗传算法优化BP神经网络的风力机涡流发生器气动性能模型,通过计算气动性能模型预测值与仿真值的误差与均方根,验证气动性能模型的可靠性;耦合鱼群算法和风力机涡流发生器气动性能模型,建立风力机涡流发生器优化方法,对涡流发生器高度、长度和安装角度进行迭代求解,实现涡流发生器优化.结果表明:相比原涡流发生器方案,涡流发生器优化后的风力机叶片截面流动分离得到有效抑制和延迟,表面流体分离现象得到改善,风力机功率提升1.711%,推力下降0.875%.

关键词: 遗传算法, 反向传播神经网络, 鱼群算法, 涡流发生器, 风力机功率

Abstract:

The optimal Latin hypercube experimental design method is used to refine the vortex generator parameters, determine the test scheme, simulate and calculate the thrust and torque of the wind turbine, and obtain the experimental data. Based on the back propagation (BP) neural network, the aerodynamic performance model of the wind turbine vortex generator optimized by genetic algorithm is constructed. The reliability of the aerodynamic performance model is verified by calculating the error and root mean square of the predicted and simulated values of the aerodynamic performance model. Coupling the fish swarm algorithm and the aerodynamic performance model of the wind turbine vortex generator, an optimization method of the wind turbine vortex generator is established, and the height, length, and installation angle of the vortex generator are solved iteratively to realize the optimization of the vortex generator. The results show that compared with the original vortex generator scheme, the flow separation of the wind turbine blade section optimized by the vortex generator is effectively restrained and delayed, the surface fluid separation phenomenon is improved, the power of the wind turbine is increased by 1.711%, and the thrust of the wind turbine is decreased by 0.875%.

Key words: genetic algorithm, back propagation (BP) neural network, fish swarm algorithm, vortex generator, wind turbine power

中图分类号:

TM315

夏云松, 谭剑锋, 韩水, 高金娥. 基于反向传播神经网络的风力机涡流发生器优化[J]. 上海交通大学学报, 2023, 57(11): 1492-1500.

XIA Yunsong, TAN Jianfeng, HAN Shui, GAO Jin’e. Optimization of Wind Turbine Vortex Generator Based on Back Propagation Neural Network[J]. Journal of Shanghai Jiao Tong University, 2023, 57(11): 1492-1500.

图/表 13

图1

图2

表1

图3

图4

图5

图6

表2

图7

表3

图8

图9

图10

参考文献 16

[1]	ZHAO Z Z, JIANG R F, FENG J X, et al. Researches on vortex generators applied to wind turbines: A review[J]. Ocean Engineering, 2022, 253: 111266. doi: 10.1016/j.oceaneng.2022.111266 URL
[2]	李爽. 风力机翼型动态失速的模型及流动控制机制研究[D]. 北京: 中国科学院大学(中国科学院工程热物理研究所), 2021.
	LI Shuang. Study of dynamic stall model and flow control mechanism of wind turbine airfoil[D]. Beijing: Institute of Physics, Chinese Academy of Sciences, 2021.
[3]	MANOLESOS M, VOUTSINAS S G. Experimental investigation of the flow past passive vortex generators on an airfoil experiencing three-dimensional separation[J]. Journal of Wind Engineering and Industrial Aerodynamics, 2015, 142: 130-148. doi: 10.1016/j.jweia.2015.03.020 URL
[4]	GAO L Y, ZHANG H, LIU Y, et al. Effects of vortex generators on a blunt trailing-edge airfoil for wind turbines[J]. Renewable Energy, 2015, 76: 303-311. doi: 10.1016/j.renene.2014.11.043 URL
[5]	TIMMER W A, VAN ROOIJ R P J O M. Summary of the delft university wind turbine dedicated airfoils[J]. Journal of Solar Energy Engineering, 2003, 125(4): 488-496. doi: 10.1115/1.1626129 URL
[6]	MUELLER-VAHL H, PECHLIVANOGLOU G, NAYERI C N, et al. Vortex generators for wind turbine blades: A combined wind tunnel and wind turbine parametric study[C]// Proceedings of ASME Turbo Expo 2012:Turbine Technical Conference and Exposition. Copenhagen, Denmark: ASME, 2012: 899-914.
[7]	DE TAVERNIER D, FERREIRA C, VIRÉ A, et al. Controlling dynamic stall using vortex generators on a wind turbine airfoil[J]. Renewable Energy, 2021, 172: 1194-1211. doi: 10.1016/j.renene.2021.03.019 URL
[8]	张惠, 赵宗德, 周广鑫, 等. 涡流发生器对风力机翼型气动性能影响的实验研究[J]. 太阳能学报, 2017, 38(4): 951-958.
	ZHANG Hui, ZHAO Zongde, ZHOU Guangxin, et al. Experimental investigation of effect of vortex generator on aerodynamic performance of wind turbine airfoil[J]. Acta Energiae Solaris Sinica, 2017, 38(4): 951-958.
[9]	张惠, 赵宗德, 周广鑫, 等. 涡发生器参数对风力机翼型性能影响实验研究[J]. 太阳能学报, 2017, 38(12): 3399-3405.
	ZHANG Hui, ZHAO Zongde, ZHOU Guangxin, et al. Experimental investigation of effect of vortex generator’s parameter on performance of wind turbine aerofoil[J]. Acta Energiae Solaris Sinica, 2017, 38(12): 3399-3405.
[10]	杨瑞, 马超善, 方亮, 等. 加装叶片涡流发生器对变桨距风力机功率的影响[J]. 兰州理工大学学报, 2019, 45(6): 64-68.
	YANG Rui, MA Chaoshan, FANG Liang, et al. Effect of installing vortex generator onto blade on power of variable-pitch wind turbine[J]. Journal of Lanzhou University of Technology, 2019, 45(6): 64-68.
[11]	FATEHI M, NILI-AHMADABADI M, NEMATOLLAHI O, et al. Aerodynamic performance improvement of wind turbine blade by cavity shape optimization[J]. Renewable Energy, 2019, 132: 773-785. doi: 10.1016/j.renene.2018.08.047 URL
[12]	赵清鑫, 张兰挺. 基于径向基神经网络的风力机叶片铺层优化[J]. 太阳能学报, 2020, 41(4): 229-234.
	ZHAO Qingxin, ZHANG Lanting. Ply parameter optimization of wind turbine blade based on radial basis function netural network[J]. Acta Energiae Solaris Sinica, 2020, 41(4): 229-234.
[13]	BI J X, FAN W Z, WANG Y, et al. A fault diagnosis algorithm for wind turbine blades based on BP neural network[J]. IOP Conference Series: Materials Science and Engineering, 2021, 1043(2): 022032. doi: 10.1088/1757-899X/1043/2/022032
[14]	HAND M M, SIMMS D A, FINGERSH L J, et al. Unsteady aerodynamics experiment phase VI: Wind tunnel test configurations and available data campaigns[R]. USA: Office of Scientific and Technical Information, 2001.
[15]	季宁, 张卫星, 于洋洋, 等. 基于Kriging代理模型和MOPSO算法的注塑成型质量多目标优化[J]. 塑料工业, 2020, 48(5): 67-71.
	JI Ning, ZHANG Weixing, YU Yangyang, et al. Multi-objective optimization of injection molding quality based on kriging agent model and MOPSO algorithm[J]. China Plastics Industry, 2020, 48(5): 67-71.
[16]	李晓磊. 一种新型的智能优化方法-人工鱼群算法[D]. 杭州: 浙江大学, 2003.
	LI Xiaolei. A new intelligent optimization method-artificial fish school algorithm[D]. Hangzhou: Zhejiang University, 2003.

序号	h/mm	l/mm	β/(°)	T/N	M/(N·m)
1	7.04	27.86	21.94	2129.69	1100.96
2	19.61	24.43	25.00	2051.26	872.01
3	17.37	15.86	6.63	2125.23	1032.05
?	?	?	?	?	?
46	22.76	13.29	10.20	2134.02	1083.02
47	24.10	24.86	12.76	2115.36	1054.24
48	20.51	10.71	21.43	2092.35	1016.70
49	9.29	17.57	7.65	2140.35	1077.87
50	16.47	25.29	15.31	2127.53	1027.86

序号	h/mm	l/mm	β/(°)	推力			转矩
序号	h/mm	l/mm	β/(°)	Y_T₁/N	Y_T₂/N	误差/%	Y_M₁/(N·m)	Y_M₂/(N·m)	误差/%
1	22.76	13.29	10.20	2134.016	2132.933	0.0510	1083.019	1083.484	0.0429
2	24.10	24.86	12.76	2115.363	2114.688	0.0320	1054.235	1053.252	0.0930
3	20.51	10.71	21.43	2092.350	2091.331	0.0490	1016.699	1016.375	0.0320
4	9.29	17.57	7.65	2140.347	2141.031	0.0319	1077.872	1078.278	0.0377
5	16.47	25.29	15.31	2127.533	2126.936	0.0280	1027.856	1024.256	0.0350

参数	VGs方案值		增幅/%
参数	原始	优化	增幅/%
T/kN	2.177	2.158	-0.875
P/kW	8.359	8.502	1.711

基于反向传播神经网络的风力机涡流发生器优化

Optimization of Wind Turbine Vortex Generator Based on Back Propagation Neural Network

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

图/表 13

参考文献 16

相关文章 15

编辑推荐

Metrics

本文评价

[1]	夏禹, 王磊. 基于反向传播神经网络的海洋工程项目投标风险评价方法[J]. 上海交通大学学报, 2023, 57(S1): 46-53.
[2]	赵志斌, 骆彬, 唐婷, 王春芳, 孙中华. 改进型自激谐振无线电能传输系统[J]. 上海交通大学学报, 2023, 57(7): 859-867.
[3]	蒋瑞民, 王宣灵, 张明恩, 赵斌. 基于量子遗传算法的反舰导弹航路规划方法[J]. 空天防御, 2023, 6(4): 31-34.
[4]	闫青, 鲁建厦, 江伟光, 邵益平, 汤洪涛, 李英德. 考虑双端口布局的紧致化仓储系统堆垛机路径优化[J]. 上海交通大学学报, 2022, 56(7): 858-867.
[5]	王箫剑, 洪君, 陈晶华, 李鸿光. 基于参数化建模和响应面优化的箱体减重研究[J]. 空天防御, 2022, 5(4): 60-66.
[6]	周天颜, 冯小恩, 范云锋, 董诗音, 李玉庆, 金慧中. 避免防空火力过剩的地面兵力防御部署优化模型[J]. 空天防御, 2022, 5(4): 19-23.
[7]	王卓鑫, 赵海涛, 谢月涵, 任翰韬, 袁明清, 张博明, 陈吉安. 反向传播神经网络联合遗传算法对复合材料模量的预测[J]. 上海交通大学学报, 2022, 56(10): 1341-1348.
[8]	周宇泰, 徐岳, 李宇, 蒋国韬. 基于遗传算法的干扰态势下三维雷达网优化布站方法[J]. 空天防御, 2022, 5(1): 52-59.
[9]	陶海红, 闫莹菲. 一种基于GA-CNN的网络化雷达节点遴选算法[J]. 空天防御, 2022, 5(1): 1-5.
[10]	李翠明, 王宁, 张晨. 基于改进遗传算法的光伏板清洁分级任务规划[J]. 上海交通大学学报, 2021, 55(9): 1169-1174.
[11]	顾一凡, 赵文龙, 唐善军, 杨擎宇, 郑鑫. 分布式主/被动成像探测系统目标空间协同定位方法研究[J]. 空天防御, 2021, 4(4): 119-126.
[12]	卓鹏程, 严瑾, 郑美妹, 夏唐斌, 奚立峰. 面向滚动轴承全生命周期故障诊断的GA-OIHF Elman神经网络算法[J]. 上海交通大学学报, 2021, 55(10): 1255-1262.
[13]	王金凤, 陈璐, 杨雯慧. 考虑设备可用性约束的单机调度问题[J]. 上海交通大学学报, 2021, 55(1): 103-110.
[14]	刘陈续, 于桂兰. 基于神经网络的层状周期结构能量传输谱预测[J]. 上海交通大学学报, 2021, 55(1): 88-95.
[15]	李强林, 曾炜杰, 田镇, 谷波. 反向传播神经网络对多结构翅片管换热器变工况性能预测适应性研究[J]. 上海交通大学学报, 2020, 54(7): 668-673.