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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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.

Key words： circular secant modified particle swarm optimization (CSM-PSO) blade vibration critical flutter system identification

Received: 20 March 2020 Published: 28 July 2021

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	Articles by authors
	LI Nailu
	YIN Jiamin
	YANG Hua
	ZHU Weijun

Cite this article:

LI Nailu,YIN Jiamin,YANG Hua, et al. 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.

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http://jvs.sjtu.edu.cn/EN/ OR http://jvs.sjtu.edu.cn/EN/Y2021/V40/I14/27

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