基于苍狼算法的感应电机改进模型参数辨识

doi:10.16182/j.issn1004731x.joss.201612019

系统仿真学报 ›› 2016, Vol. 28 ›› Issue (12): 3010-3018.doi: 10.16182/j.issn1004731x.joss.201612019

基于苍狼算法的感应电机改进模型参数辨识

吕小意, 黄松, 王艳, 纪志成

江南大学物联网技术应用教育部工程研究中心,江苏无锡 214122

收稿日期:2015-12-31 修回日期:2016-03-28 出版日期:2016-12-08 发布日期:2020-08-13
作者简介:吕小意(1991-),男,湖北广水,硕士,研究方向为感应电机控制及参数辨识;黄松(1984-),男,湖北随州,博士,研究方向为智能控制和智能算法。
基金资助:
国家自然科学基金(61572238), 国家高技术研究发展计划(2014AA041505), 江苏省杰出青年基金(BK20160001)

Grey Wolf Optimizer for Parameters Identification of Induction Motor with Improved Model

Lü Xiaoyi, Huang Song, Wang Yan, Ji Zhicheng

Engineering Research Center of Internet of Things Technology Applications Ministry of Education, Jiangnan University, Wuxi 214122, China

Received:2015-12-31 Revised:2016-03-28 Online:2016-12-08 Published:2020-08-13

摘要/Abstract

摘要： 为解决感应电机高性能控制中参数估计不准的问题,提出基于苍狼算法的感应电机参数辨识方法。该优化算法是一种新颖的元启发式算法,运用起来简单、灵活,需调节参数少。考虑到两种经典动态数学模型对不同参数辨识精度影响不同,提出了一种改进的感应电机参数辨识模型。相对于经典模型,仿真表明提出的模型使电阻特别是定子电阻的辨识效果得到较大提升,证明改进模型的有效性。在改进的模型下,将该算法与粒子群算法、遗传算法进行电机参数辨识的对比实验。实验结果表明苍狼算法具有较高的辨识精度,证明应用该算法辨识感应电机参数的可行性。

关键词: 感应电机, 参数辨识, 苍狼算法, 定子电阻

Abstract: According to the problems of inaccurate parameters estimation of the induction motor in high-performance control, a grey wolf optimizer was used to identify the parameters of the induction motor. Grey wolf optimizer is a new meta-heuristic. It is simple and flexible to implement, and has fewer parameters to tune. Considering that two typical dynamic mathematical models have different identification precision on different parameters, the improved identification model of the induction motor was proposed. Compared with typical model, simulation results show that the proposed model obviously improves the identification performance of resistances especially stator resistance, verifying the validity of improved model. The algorithm was compared with particle swarm optimization and genetic algorithm for parameters identification of the induction motor with the improved model. Experimental results show that grey wolf optimizer has higher identification precision, demonstrating that parameters identification of the induction motor based on this algorithm is feasible.

Key words: induction motor, parameter identification, grey wolf optimizer, stator resistance

中图分类号:

TP391.9

吕小意, 黄松, 王艳, 纪志成. 基于苍狼算法的感应电机改进模型参数辨识[J]. 系统仿真学报, 2016, 28(12): 3010-3018.

Lü Xiaoyi, Huang Song, Wang Yan, Ji Zhicheng. Grey Wolf Optimizer for Parameters Identification of Induction Motor with Improved Model[J]. Journal of System Simulation, 2016, 28(12): 3010-3018.

参考文献

[1] Martínez-Hernández M A, Hernández-Guzmán V M, Carrillo-Serrano R V, et al. A slightly modified indirect field oriented controller for voltage-fed induction motors with a global asymptotic stability proof[J]. European Journal of Control (S0947-3580), 2015, 11(1): 60-68.
[2] 徐奇伟, 宋立伟, 崔淑梅. 感应电机矢量控制中转子参数自适应辨识[J]. 电工技术学报, 2011, 26(6): 81-87.
(Xu Qiwei, Song Liwei, Cui Shumei.Induction Motor Vector Control Based on Adaptive Identification of Rotor Parameters[J]. Transactions of China Electrotechnical Society (S1000-6753), 2011, 26(6): 81-87.)
[3] He Y, Wang Y, Feng Y, et al.Parameter Identification of an Induction Machine at Standstill Using the Vector Constructing Method[J]. IEEE Transactions on Power Electronics (S0885-8993), 2012, 27(2): 905-915.
[4] Wang S, Dinavahi V, Xiao J.Multi-rate real-time model-based parameter estimation and state identification for induction motorsv[J]. Iet Electric Power Applications (S1751-8660), 2013, 7(1): 77-86.
[5] Kojabadi H M.Active power and MRAS based rotor resistance identification of an IM drive[J]. Simulation Modeling Practice and Theory (S1569-190X), 2009, 17(2): 376-389.
[6] Huang K S, Kent W, Wu Q H, et al.Parameter identification for FOC induction motors using genetic algorithms with improved mathematical model[J]. Electric Power Components and Systems (S1532-5008), 2001, 29(3): 247-258.
[7] Chen Z, Zhong Y, Li J.Parameter identification of induction motors using Ant Colony Optimization[C]// Evolutionary Computation (S1063-6560). IEEE, 2008: 1611-1616.
[8] Bai J, Liu H, Yin B, et al.Modeling of enhanced VFAs production from waste activated sludge by modified ADM1 with improved particle swarm optimization for parameters estimation[J]. Biochemical Engineering Journal (S1369-703X), 2015, 103(1): 22-31.
[9] Sakthivel V P, Bhuvaneswari R, Subramanian S.Multi-objective parameter estimation of induction motor using particle swarm optimization[J]. Engineering Applications of Artificial Intelligence (S0952-1976), 2010, 23(3): 302-312.
[10] Bechouche A, Sediki H, Abdeslam D O, et al.Identification of induction motor at standstill using artificial neural network[C]// IECON 2010-36th Annual Conference on IEEE Industrial Electronics Society (S1553-572X). IEEE, 2010: 2908-2913.
[11] 张杰, 柴建云, 孙旭东, 等. 基于参数在线校正的电动汽车异步电机间接矢量控制[J]. 电工技术学报, 2014, 29(7): 90-96.
(Zhang Jie, Chai Jianyun, Sun Xudong, et al.Indirect Vector Control of Induction Motor Drives for Electric ehicles Based on Parameters On-Line Tuning[J]. Transactions of China Electrotechnical Society (S1000-6753), 2014, 29(7): 90-96.)
[12] Mirjalili S, Mirjalili S M, Lewis A.Grey wolf optimizer[J]. Advances in Engineering Software (S0965-9978), 2014, 69(3): 46-61.
[13] Song X, Tang L, Zhao S, et al.Grey Wolf Optimizer for parameter estimation in surface waves[J]. Soil Dynamics & Earthquake Engineering (S0267-7261), 2015, 75(1): 147-157.
[14] Sharma Y, Saikia L C.Automatic generation control of a multi-area ST - Thermal power system using Grey Wolf Optimizer algorithm based classical controllers[J]. International Journal of Electrical Power & Energy Systems (S0142-0615), 2015, 73(1): 853-862.
[15] Chaman-Motlagh A.Superdefect Photonic Crystal Filter Optimization Using Grey Wolf Optimizer[J]. IEEE Photonics Technology Letters (S1041-1135), 2015, 27(22): 2355-2358.
[16] 傅小利, 顾红兵, 陈国呈, 等. 基于柯西变异粒子群算法的永磁同步电机参数辨识[J]. 电工技术学报, 2014, 29(5): 127-131.
(Fu Xiaoli, Gu Hongbing, Chen Guocheng, et al.Permanent Magnet Synchronous Motors Parameters Identification Based on Cauchy Mutation Particle Swarm Optimization[J]. Transactions of China Electrotechnical Society (S1000-6753), 2014, 29(5): 127-131.)
[17] 阮毅, 陈伯时. 电力拖动自动控制系统: 运动控制系统 [M]. 北京:机械工业出版社, 2010.
(Ruan Yi, Chen Boshi.Control Systems of Electric Drives: Motion Control Systems [Z]. Beijing, China: China Machine Press, 2010.)

基于苍狼算法的感应电机改进模型参数辨识

Grey Wolf Optimizer for Parameters Identification of Induction Motor with Improved Model

PDF (PC)

可视化

被引次数

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	徐鹏, 冯国奇, 代学武, 崔东亮, 魏七龙, 李宝旭, 李建明. 高铁限速运行时分的小数据驱动建模与仿真[J]. 系统仿真学报, 2021, 33(8): 1892-1904.
[2]	韩凯伟, 王艳, 纪志成. 一种直线感应电机的新型积分滑模控制器的设计与仿真[J]. 系统仿真学报, 2020, 32(11): 2146-2154.
[3]	李琳, 林燕龙, 邹焱飚. Scara机器人关节摩擦建模与补偿的实验与仿真[J]. 系统仿真学报, 2019, 31(8): 1572-1581.
[4]	钟晶亮, 苟小龙, 邓彤天. 汽轮机及调节系统参数直接辨识法研究及应用[J]. 系统仿真学报, 2018, 30(9): 3312-3318.
[5]	沈艳霞, 靳保龙. 永磁同步电机模糊遗忘因子最小二乘法参数辨识[J]. 系统仿真学报, 2018, 30(9): 3404-3410.
[6]	吴定会, 黄旭, 全亚威, 纪志成. 基于变异珊瑚礁算法的永磁同步电机参数辨识[J]. 系统仿真学报, 2018, 30(8): 3024-3032.
[7]	陈锦宝, 李杰, 王艳, 纪志成. 融合Levy飞行的教与学优化算法的PMSM参数辨识[J]. 系统仿真学报, 2018, 30(4): 1456-1463.
[8]	吴定会, 张建宇, 沈艳霞, 纪志成. 基于多新息近似最小一乘算法PMSM参数辨识[J]. 系统仿真学报, 2018, 30(3): 1001-1007.
[9]	王梦秋, 王艳, 纪志成. 基于改进樽海鞘群算法的PMSM多参数辨识[J]. 系统仿真学报, 2018, 30(11): 4284-4292.
[10]	李杰, 王艳, 纪志成. 基于改进教与学优化算法的永磁同步电机参数辨识[J]. 系统仿真学报, 2017, 29(2): 393-401.
[11]	宁方鑫, 熊勇, 牟军敏, 黄兴东. 基于AIS数据的船舶操纵性指数辨识研究[J]. 系统仿真学报, 2017, 29(2): 402-408.
[12]	贺智, 聂志成, 秦怡宁, 樊友平. 基于拉格朗日乘子和测量残差的参数辨识[J]. 系统仿真学报, 2017, 29(12): 2964-2971.
[13]	吕小意, 王艳, 纪志成, 严大虎. 感应电机变结构PID速度控制及转子电阻辨识[J]. 系统仿真学报, 2017, 29(12): 3132-3138.
[14]	蔡磊, 王艳, 纪志成. 基于折息递推辨识算法的数控机床能效预测[J]. 系统仿真学报, 2016, 28(8): 1725-1731.
[15]	黄松, 田娜, 王艳, 纪志成. 基于比例策略的多目标PSO的感应电机参数辨识[J]. 系统仿真学报, 2016, 28(7): 1489-1496.