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基于并行动态学习型免疫算法的永磁同步电机状态监测

刘朝华 李小花 张红强 周少武

刘朝华, 李小花, 张红强, 周少武. 基于并行动态学习型免疫算法的永磁同步电机状态监测. 自动化学报, 2015, 41(7): 1283-1294. doi: 10.16383/j.aas.2015.c140678
引用本文: 刘朝华, 李小花, 张红强, 周少武. 基于并行动态学习型免疫算法的永磁同步电机状态监测. 自动化学报, 2015, 41(7): 1283-1294. doi: 10.16383/j.aas.2015.c140678
LIU Zhao-Hua, LI Xiao-Hua, ZHANG Hong-Qiang, ZHOU Shao-Wu. Parallel Dynamic Learnable Immune Evolutionary Algorithm for Permanent Magnet Synchronous Machine Parameter Condition Monitoring. ACTA AUTOMATICA SINICA, 2015, 41(7): 1283-1294. doi: 10.16383/j.aas.2015.c140678
Citation: LIU Zhao-Hua, LI Xiao-Hua, ZHANG Hong-Qiang, ZHOU Shao-Wu. Parallel Dynamic Learnable Immune Evolutionary Algorithm for Permanent Magnet Synchronous Machine Parameter Condition Monitoring. ACTA AUTOMATICA SINICA, 2015, 41(7): 1283-1294. doi: 10.16383/j.aas.2015.c140678

基于并行动态学习型免疫算法的永磁同步电机状态监测

doi: 10.16383/j.aas.2015.c140678
基金项目: 

国家科技支撑计划(2012BAH09B02),国家自然科学基金(61174140, 51374107),中 国博士后科学基金(2013M540628, 2014T70767),湖南省自然科学基金(13JJ8014, 14JJ3107),湖南省教育厅科研优秀青年项目(15B087)资助

详细信息
    作者简介:

    李小花硕士, 湖南科技大学信息与电气工程学院讲师. 主要研究方向为复杂工业过程控制与优化, 网络安全.E-mail: teacherli163@163.com

Parallel Dynamic Learnable Immune Evolutionary Algorithm for Permanent Magnet Synchronous Machine Parameter Condition Monitoring

Funds: 

Supported by Key Projects in the National Science and Technology Pillar Program (2012BAH09B02), National Natural Science Foundation of China (61174140, 51374107), China Postdoctoral Science Foundation Funded Project (2013M540628, 2014T70767), Hunan Provincial Natural Science Foundation of China (13JJ8014, 14JJ3107), and Hunan Provincial Education Department outstanding youth project (15B087)

  • 摘要: 为提高永磁同步电机(Permanent magnet synchronous machine, PMSM)系统参数辨识与状态监测效率,利用图形处理器(Graphics processing unit, GPU)并行计算与 人工免疫技术相结合的研究方法,建立面向永磁同步电机系统基于GPU并行动态学习型 免疫进化的参数估计与状态监测模型.为提高算法的动态跟踪性能,在抗体演化进 程中,通过知识学习策略来引导算法进化过程,首先将抗体群划分为B细胞群、浆细胞 群以及记忆细胞群,对处于不同进化群体中的抗体分别设计免疫综合学习策略、免 疫反向学习策略和高斯学习策略,以增强抗体间的信息交互;接着,应用图形处 理器并行计算技术进一步加速算法求解过程;最后,将所提算法应用于永磁同 步电机系统参数辨识与状态监测中,实验表明,所提方法能同时准确地对电机的定子 电阻、dq轴电感和永磁磁链等系统关键参数进行估计.依据参数变化实现对系统 运行状态进行在线监测与预警.计算结果表明, GPU并行技术能大幅度提高计算效率.
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出版历程
  • 收稿日期:  2014-10-08
  • 修回日期:  2015-01-19
  • 刊出日期:  2015-07-20

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