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.

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参考文献 16

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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

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