基于圆周割线改进型粒子群优化算法的叶片临界颤振辨识方法研究

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振动与冲击 ›› 2021, Vol. 40 ›› Issue (14) : 27-34.

论文

李迺璐，尹佳敏，杨华，朱卫军

作者信息 +

Method of identifying limit cycle oscillations of blades based on circular secant modified particle swarm optimization

LI Nailu，YIN Jiamin，YANG Hua，ZHU Weijun

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文章历史 +

摘要

针对风力机叶片在颤振风速下的临界颤振现象，创新性地结合几何圆周割线和传统粒子群优化算法，首次设计了一种圆周割线改进型粒子群优化算法，应用于叶片临界颤振系统的参数辨识。该方法利用圆周上移动点的割线距离来动态调节全局学习因子和局部学习因子，针对优化辨识提高全局搜索和局部搜索的动态平衡性，避免陷入局部最优，提高算法的整体寻优性能和优化效率。仿真试验中，将该方法与多种先进粒子群优化算法(如改进型粒子群优化(MPSO)算法、基于线性递减惯性权重的粒子群优化(LDIW-PSO)算法、基于动态学习因子的免疫粒子群优化(IPSODCLF)算法)的辨识结果相比较，结果表明该辨识方法在辨识精度、计算时间和鲁棒性方面均具有显著的优越性。

Abstract

In view of the critical flutter of wind turbine blades at flutter speed, a circular secant modified particle swarm optimization (CSM-PSO) algorithm was designed by combining the geometric circular secant with the traditional PSO algorithm.The proposed CSM-PSO was applied to the parameter identification of a blade critical flutter system.In the method, the moving circumscribed distance was used to adjust the global learning factor and the local learning factor to improve the dynamic balance between the global search and the local search for the optimization identification.It is found that in this way, the algorithm can avoid falling into the local optimization and improve the overall optimization performance and computational efficiency.In simulation experiments, the results of the method were compared with the results of existing modified PSOs, such as modified particle swarm optimization(MPSO), linearly decreasing inertia weight based particle swarm optimization(LDIW-PSO) and immune particle swarm optimization based on dynamically changing learning factors(IPSODCLF).The results show that the method has significant improvement in identification accuracy, computation time and robustness.

导出引用

李迺璐，尹佳敏，杨华，朱卫军. 基于圆周割线改进型粒子群优化算法的叶片临界颤振辨识方法研究[J]. 振动与冲击, 2021, 40(14): 27-34

LI Nailu，YIN Jiamin，YANG Hua，ZHU Weijun. Method of identifying limit cycle oscillations of blades based on circular secant modified particle swarm optimization[J]. Journal of Vibration and Shock, 2021, 40(14): 27-34

参考文献

［1］李迺璐,穆安乐,BALAS M J.基于B-L气动模型的旋转水平风机叶片经典颤振稳定性分析［J］.振动与冲击, 2015,34(23): 171-176.
LI Nailu, MU Anle, BALAS M J.Classical flutter stability of rotating horizontal wind turbine blades based on B-L aeroelastic model［J］.Journal of Vibration and Shock, 2015,34(23): 171-176.

［2］LI N L, BALAS M J.Aeroelastic control of wind turbine blade using trailing-edge flap［J］.Wind Engineering, 2014,38(5): 549-560.

［3］常林, 刘廷瑞, 徐玮, 等.大型水平轴风力机叶片气弹稳定性研究及控制［J］.机械设计与研究, 2018, 34(2): 199-204.
CHANG Lin, LIU Tingrui, XU Wei, et al.Research and control of blade aeroelastic stability of large horizontal axis wind turbine［J］.Machine Design and Research, 2018,34(2): 199-204.

［4］张家铭, 杨执钧, 黄锐.基于非线性状态空间辨识的气动弹性模型降阶［J］.力学学报, 2020,52(1): 150-161.
ZHANG Jiaming, YANG Zhijun, HUANG Rui.Reduced-order modeling for aeroelastic systems via nonlinear state-space identification［J］.Chinese Journal of Theoretical and Applied Mechanics, 2020,52(1): 150-161.

［5］冀然.基于神经网络的非线性气动弹性系统的预测控制［D］.天津：天津大学，2016.

［6］黄灿, 赵永辉.基于CFD系统辨识的气弹分析及GPU并行算法初探［J］.动力学与控制学报, 2015(2): 86-91.
HUANG Can, ZHAO Yonghui.Identification of CFD-based aeroelastic analysis and GPU parallel computing［J］.Journal of Dynamics and Control, 2015(2): 86-91.

［7］李治涛, 韩景龙, 员海玮.基于Hammerstein模型的非线性气动弹性系统辨识［J］.南京航空航天大学学报, 2013,45(1): 14-20.
LI Zhitao, HAN Jinglong, YUAN Haiwei.Identification of nonlinear aeroelastic systems based on Hammerstein model［J］.Journal of Nanjing University of Aeronautics and Astronautics, 2013,45(1): 14-20.

［8］陈康,张娅,王维民,等.基于虚拟传感器内插法的叶片高倍频振动辨识方法［J］.机械工程学报, 2019,55(19): 1-8.
CHEN Kang, ZHANG Ya, WANG Weimin, et al.Identification method of blade vibration with high frequency based on virtual sensor interpolation［J］.Journal of Mechanical Engineering, 2019,55(19): 1-8.

［9］周金龙,董凌华,杨卫东,等.基于加权最小二乘法辨识的后缘襟翼智能旋翼振动载荷闭环控制仿真研究［J］.振动与冲击, 2019,38(4): 237-244.
ZHOU Jinlong, DONG Linghua, YANG Weidong, et al.Research and control of blade aeroelastic stability of large horizontal axis wind turbine［J］.Journal of Vibration and Shock, 2019,38(4): 237-244.

［10］欧阳涛,郭文力,段发阶,等.基于叶尖定时的旋转叶片同步振动辨识新方法［J］.振动与冲击, 2011,30(8): 249-252.
OUYANG Tao, GUO Wenli, DUAN Fajie, et al.New method for identifying rotating blades synchronous vibration based on tip-timing［J］.Journal of Vibration and Shock, 2011,30(8): 249-252.

［11］段发阶, 闫明, 李孟麟, 等.双参数法辨识叶片同步振动的研究［J］.传感器与微系统, 2010,29(3): 42-45.
DUAN Fajie, YAN Ming, LI Menglin, et al.Research on identifying synchruous blade vibration using two-parameter-plot method［J］.Transducer and Microsystem Technologies, 2010,29(3): 42-45.

［12］李迺璐, 徐燕，徐庆，等.基于DE优化系统辨识的风力机叶片自校正PID振动控制［J］.振动与冲击, 2018,37(6): 193-201.
LI Nailu, XU Yan, XU Qing, et al.Vibration control of wind turbine blades based on the self-tuning PID control and differential evolution algorithm［J］.Journal of Vibration and Shock, 2018,37(6): 193-201.

［13］KENNEDY J R, EBERHART R.Particle swarm optimization［C］//Proceedings of IEEE International Conference on Neural Networks.Pisca-taway: IEEE, 1995.

［14］JORDEHI R A.A review on constraint handling strategies in particle swarm optimisation［J］.Neural Computing and Applications, 2015,26(6): 1265-1275.

［15］CHEN D B, WANG J T, FENG Z, et al.Linguistic fuzzy model identification based on PSO with different length of particles［J］.Applied Soft Computation, 2012,12(11): 3390-3400.

［16］王峰,周宜红,赵春菊,等.基于改进粒子群优化算法的混凝土坝热学参数反演研究［J］.振动与冲击, 2019,38(12): 168-174.
WANG Feng, ZHOU Yihong, ZHAO Chunju, et al.Inverse analysis of concrete dam thermal parameters based on an improved partical swarm optimization method［J］.Journal of Vibration and Shock, 2019,38(12): 168-174.

［17］马国庆,李瑞峰,刘丽.学习因子和时间因子随权重调整的粒子群优化算法［J］.计算机应用研究, 2014,31(11): 3291-3294.
MA Guoqing, LI Ruifeng, LIU Li.Partical swarm optimization algorithm of learning factors and time factor adjusting to weights［J］.Application Research of Computers, 2014,31(11): 3291-3294.

［18］徐生兵,夏文杰,代安定.一种改进学习因子的粒子群优化算法［J］.信息安全与技术, 2012,3(7): 17-19.
XU Shengbing, XIA Wenjie, DAI Anding.A new modified acceleration coefficient in PSO［J］.Information Security, 2012,3(7): 17-19.

［19］罗毅,张若含.基于动态调整学习因子的免疫粒子群优化算法及其应用［J］.电网与清洁能源, 2014,30(2): 76-80.
LUO Yi, ZHANG Ruohan.Immune partical swarm optimization and application based on dynamically changing learing factors［J］.Power System and Clean Energy, 2014,30(2): 76-80.

［20］卫晓娟, 丁旺才, 李宁洲, 等.基于改进粒子群优化算法的Volterra模型参数辨识［J］.振动与冲击, 2015,34(21): 113-120.
WEI Xiaojuan, DING Wangcai, LI Ningzhou, et al.Parametric identification of nonlinear volterra model based on improved PSO algorithm［J］.Journal of Vibration and Shock, 2015,34(21): 113-120.