摘要
为提高抽水蓄能机组调节系统非线性模型参数辨识的精度和速度,对改进的粒子群算法(IPPSO)进行了研究。通过反三角函数初始化,改进鸽群搜索算子更新粒子以及柯西公式变异粒子,综合改善算法的搜索性能。与遗传算法、万有引力搜索算法、标准粒子群算法的对比仿真实验表明,改进后的算法具有更快的收敛速度及更高的辨识精度,为抽水蓄能机组调节系统的非线性辨识提供了新方法。
In order to improve the accuracy and speed of nonlinear model parameter identification of pumped storage unit governing system,the improved parallel particle swarm optimization(IPPSO)algorithm is studied.Through the initialization of inverse trigonometric function,the particle of pigeon group search operator and the variation particle of Cauchy formula are improved,and the search performance of the algorithm is improved comprehensively.Compared with genetic algorithm,gravitational search algorithm and standard particle swarm optimization algorithm,the simulation results show that the improved algorithm has faster convergence speed and higher identification accuracy,which provides a new method for nonlinear identification of pumped storage unit governing system.
作者
于浩
高翔
刘晓
YU Hao;GAO Xiang;LIU Xiao(School of Mechanical Electronic&Information Engineering,China University of Mining and Technology-Beijing,Beijing 100083,China)
出处
《现代信息科技》
2021年第3期162-165,共4页
Modern Information Technology
关键词
调速系统
非线性
参数辨识
粒子群
speed-governing system
nonlinear
parameter identification
particle swarm
