摘要
为提高弹性水击下的水轮机调速系统非线性模型参数辨识的精度,提出了基于莱维飞行改进粒子群算法(LF-PSO)对模型进行参数辨识。由于传统的PSO算法存在一定的缺陷,易陷入局部最优,导致辨识效果不理想,加入莱维飞行可有效改善此类问题,提高参数的寻优能力。为验证辨识结果的有效性,将LF-PSO算法的辨识效果与PSO算法进行了对比分析,结果表明LF-PSO算法可用于水轮机调速系统参数辨识,且辨识的精度更高,算法更为稳定,收敛速度也更快。
In order to improve the accuracy of parameter identification of nonlinear model of hydraulic turbine speed governing system under elastic water hit,the Levy flight improved particle swarm optimization algorithm(LF-PSO)is proposed to identify the model parameters.Since the traditional PSO algorithm has some defects,it is easy to fall into local optimization,which leads to the unsatisfactory identification effect,the addition of Levy flight can effectively improve such problems and improve the ability of parameter optimization.In order to verify the validity of the identification results,the identification effect of LF-PSO algorithm and PSO algorithm are compared and analyzed.The results show that the LF-PSO algorithm can be used for the parameter identification of hydraulic turbine speed governing system,and the identification accuracy is higher,the algorithm is more stable and the convergence speed is faster.
作者
王伟
曾云
钱晶
于磊
邹屹东
郭志成
WANG Wei;ZENG Yun;QIAN Jing;YU Lei;ZOU Yidong;GUO Zhicheng(College of Metallurgy and Energy Engineering,Kunming University of Science and Technology,Kunming 650093,Yunnan,China)
出处
《水力发电》
CAS
2021年第7期83-88,共6页
Water Power
基金
国家自然科学基金资助项目(52079059,51869007)。
关键词
水轮机
调速系统
非线性模型
参数辨识
粒子群算法
莱维飞行
hydraulic turbine
speed governing system
nonlinear model
parameter identification
particle swarm optimization
Levy flight
