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智能制造21年3期

抽水蓄能机组非线性模型参数辨识
于浩,高翔,刘晓
(中国矿业大学(北京) 机电与信息工程学院,北京 100083)

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。


关键词:调速系统;非线性;参数辨识;粒子群



DOI:10.19850/j.cnki.2096-4706.2021.03.042


中图分类号:TV734;TM341                       文献标识码:A                                     文章编号:2096-4706(2021)03-0162-04


Parameter Identification of Nonlinear Model of Pumped Storage Unit

YU Hao,GAO Xiang,LIU Xiao

(School of Mechanical Electronic & Information Engineering,China University of Mining and Technology-Beijing, Beijing 100083,China)

Abstract: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.

Keywords:speed-governing system;nonlinear;parameter identification;particle swarm


参考文献:

[1] 吴洪涛,何宗卿,朱亮,等 . 基于最小二乘法的永磁同步 电机参数辨识 [J]. 电子技术,2021,50(2):48-49.

[2] 王珊,周建中,杜思存,等 . 基于 RBF 神经网络的水轮 机调节系统辨识 [J]. 水力发电,2006(3):42-44.

[3] 魏加富,贾珍,程远楚 . 基于遗传算法的转桨式水轮机模 型参数辨识 [J]. 水电能源科学,2018,36(5):133-136.

[4] SHI Y,EBERHART R.A modified particle swarm optimizer [C]//1998 IEEE International Conference on Evolutionary Computation Proceedings.IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).Anchorage:IEEE,1998:69-73.

[5] 张国富,蒋建国,齐美彬,等 . 基于粒子群算法求解多层 非线性规划问题 [J]. 模式识别与人工智能,2007,20(6):745- 750.

[6] DU W,YING W,YAN G,et al. Heterogeneous Strategy Particle Swarm Optimization [J].IEEE Transactions on Circuits and Systems II:Express Briefs,2017,64(4):467-471.

[7] CHEN S W,XU Z M,TANG Y,et al. An Improved Particle Swarm Optimization Algorithm Based on Centroid and Exponential Inertia Weight [J/OL].Mathematical Problems in Engineering,2014: [2020-11-26].https://doi.org/10.1155/2014/976486.

[8] 王德成,林辉 . 一种基于轨道均匀分布的混沌遗传优化算 法 [J]. 计算机应用研究,2009,26(4):1292-1293.

[9] 王金杰 . 基于多目标粒子群优化算法的研究与应用 [D]. 合肥:安徽大学,2020.

[10] 段海滨,邱华鑫,范彦铭 . 基于捕食逃逸鸽群优化的无 人机紧密编队协同控制 [J]. 中国科学:技术科学,2015,45(6): 559-572.

[11] QIU H X,DUAN H B. Multi-objective pigeon-inspired optimization for brushless direct current motor parameter design [J]. Science China Technological Sciences,2015,58:1915-1923.

[12] 马龙,卢才武,顾清华,等 . 引入改进鸽群搜索算子的粒 子群优化算法 [J]. 模式识别与人工智能,2018,31(10):909-920.


作者简介:于浩(1994—),男,汉族,山东日照人,研究生 在读,研究方向:控制理论与控制工程;高翔(1993—),男,汉族, 山西太原人,研究生在读,研究方向:电气工程;刘晓(1995—), 女,汉族,四川什邡人,研究生在读,研究方向:控制理论与控 制工程。