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。
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.
作者
李迺璐
尹佳敏
杨华
朱卫军
LI Nailu;YIN Jiamin;YANG Hua;ZHU Weijun(College of Electrical,Energy and Power Engineering,Yangzhou University,Yangzhou 225127,China)
出处
《振动与冲击》
EI
CSCD
北大核心
2021年第14期27-34,共8页
Journal of Vibration and Shock
基金
国家自然科学基金(11672261)
江苏省自然科学青年基金(BK20180891)
扬州市自然科学基金(YZ2018101)
扬州大学青蓝工程优秀青年骨干教师项目。
