基于电网侧电流的PMSM电气故障诊断  

Electrical Fault Diagnosis of PMSM Based on Grid Side Current

作　　者：熊勇虎 姚维[1] 许海波 XIONG Yonghu;YAO Wei;XU Haibo(College of Electrical Engineering,Zhejiang University,Hangzhou 310027,China;Hangzhou Kangbei Motor Co.,Ltd.,Hangzhou 311121,China)

机构地区：[1]浙江大学电气工程学院,杭州310027 [2]杭州康钡电机有限公司,杭州311121

出　　处：《微特电机》2024年第12期59-64,共6页Small & Special Electrical Machines

摘　　要：实际应用中,从永磁同步电机侧引出信号进行故障诊断存在诸多不便,针对该问题提出了一种基于电网侧电流的真有效值和改进的卷积神经网络的永磁同步电机故障诊断方法。通过计算每个周期的电网侧电流的真有效值,实现PMSM正常和故障状态的快速区分;在不同故障状态模式下收集电流信号,将其转化为灰度图像并利用滑动窗口采样技术实现对样本的扩充;将这些图像输入至CNN进行故障类型的自动识别。进行仿真和实验验证,此方法在检测PMSM不同的电气故障状态时,分类正确率可达95%以上,证明了其在实际应用中的高效性和可靠性。It is not convenient for PMSM to use the signal on the motor side for fault diagnosis in practical applications.To solve the problem,a PMSM fault diagnosis method based on the root mean square value of grid side current and improved CNN was proposed.A fast differentiation between normal and faulty states of the PMSM was realized by calculating the root mean square value of the grid side current for each cycle.The current signals were collected in different fault state modes,transformed into grayscale images,and the samples were expanded by using the sliding window sampling method.These images were inputted to the CNN for automatic fault type identification.Simulation and experimental validation were carried out,the classification accuracy of this method in detecting different electrical fault states of PMSM can reach over 95%,which proves its efficiency and reliability in practical applications.

关 键 词：永磁同步电机 电气故障 卷积神经网络 灰度图像 电网侧电流 

分 类 号：TM341[电气工程—电机]