摘要
针对小功率单相永磁同步电容电机的实际应用场合,研究一种在线自学习故障检测方法。以电容电机为被控对象,建立了其数学模型,推导了归算后对称机的相电压对称分量及相电流正负序分量,并以此计算相电压及相电流;根据BP网络神经建立起自学习网络层,详细介绍了BP网络神经算法实施过程,分析了电机运行过程中的模式切换。最后,对随机抽取的3个电机进行了实验验证,结果表明该方法能够准确地检测出电机故障。
A method of fault detection which was applied to the single phase permanent magnet synchronous motor (PMSM) based on self-learning was proposed. The mathematical model was established based on the capacitor motor. Symmetrical components of phase voltage and component of positive sequence and negative sequence of phase current were derived after the motor was transformed into a symmetrical support vector machine, and the phase voltage and phase current could be figured up accordingly. The structure of self-learning network has been figured out based on BP network. The ac-tualization of arithmetic was introduced. And the operational mode was sketched out. The experiments on three motors show that the fault can be diagnosed accurately and effectively by this method.
作者
刘亚兵
胡利民
胡钦龙
孙红波
LIU Ya-bing;HU Li-min;HU Qin-long;SUN Hong-bo(Kunming Branch of the 705 Research Institute,China Shipbuilding Industry Corporation, Kunming 650101,China)
出处
《微特电机》
2019年第1期17-19,24,共4页
Small & Special Electrical Machines
关键词
单相永磁同步电机
自学习
故障检测
BP网络神经
堵转
single phase permanent magnet synchronous motor
self-learning
fault detection
BP (Back Propaga-tion) network
locked-rotor
