吴忠强,杜春奇,李峰,张伟.基于蝙蝠算法的永磁同步电机健康状态监测[J].仪器仪表学报,2017,38(3):695-702
基于蝙蝠算法的永磁同步电机健康状态监测
Health condition monitoring of the permanent magnet synchronous motor based on bat algorithm
  
DOI:
中文关键词:  永磁同步电机  健康状态监测  参数辨识  蝙蝠算法  多智能体系统
英文关键词:permanent magnet synchronous motor  health condition monitoring  parameter identification  bat algorithm  multi agent system
基金项目:国家自然科学基金(U1260203)、河北省“三三三人才工程”基金(A2016015002)、河北省自然科学基金(F2016203006)项目资助
作者单位
吴忠强 燕山大学电气工程学院 工业计算机控制工程河北省重点实验室秦皇岛066004 
杜春奇 燕山大学电气工程学院 工业计算机控制工程河北省重点实验室秦皇岛066004 
李峰 燕山大学电气工程学院 工业计算机控制工程河北省重点实验室秦皇岛066004 
张伟 燕山大学电气工程学院 工业计算机控制工程河北省重点实验室秦皇岛066004 
AuthorInstitution
Wu Zhongqiang Key Lab of Industrial Computer Control Engineering of Hebei Province,College of Electrical Engineering, Yanshan University,Qinhuangdao 066004,China 
Du Chunqi Key Lab of Industrial Computer Control Engineering of Hebei Province,College of Electrical Engineering, Yanshan University,Qinhuangdao 066004,China 
Li Feng Key Lab of Industrial Computer Control Engineering of Hebei Province,College of Electrical Engineering, Yanshan University,Qinhuangdao 066004,China 
Zhang Wei Key Lab of Industrial Computer Control Engineering of Hebei Province,College of Electrical Engineering, Yanshan University,Qinhuangdao 066004,China 
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中文摘要:
      永磁同步电机健康状态监测问题可转化为永磁同步电机多参数辨识问题。为提高系统参数辨识及状态监测效率,提出一种基于多智能体蝙蝠算法的永磁同步电机参数辨识方法。多智能体的邻域竞争合作算子实现了蝙蝠个体间的信息交流,提高了全局寻优能力及算法的动态跟踪性能;自学习算子提高算法局部寻优能力,加快算法收敛速度。永磁同步电机多参数辨识结果表明,多智能体蝙蝠算法能快速有效地辨识电机各参数,依据参数变化实现对电机运行状态的监测及预警。与未改进算法相比,验证了改进算法的有效性和优越性能。
英文摘要:
      Health condition monitoring problem of permanent magnet synchronous motor can be treated as a multi parameter identification problem of the permanent magnet synchronous motor. In order to improve the efficiency of system parameter identification and state monitoring, a kind of health condition monitoring method of the permanent magnet synchronous motor is proposed based on the multi agent bat algorithm. The competition and cooperation operation of multi agents enhances the communication of agents, and the ability of global optimization and the dynamic tracking performance of the algorithm is improved. Self study operation can improve the local search ability and the convergence rate of the algorithm. Multi parameter identification results of the permanent magnet synchronous motor show that the multi agent bat algorithm can quickly and efficiently identify parameters of the motor. The task of monitoring and early warning can be implemented for the running permanent magnet synchronous machine according to changed parameters. Compared with the unmodified algorithm, the improved algorithm shows the effectiveness and superior performance.
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