欢迎来到中国电源学会电子资源平台
会员 Imbalanced Samples Fault Detection Using GAN-CNN for Power Inverters
  • 8
  • 0
  • 0
  • 0
  • 2022/01/01
摘要
Aiming at the problem of incomplete fault types existing in power switches fault detection for three phase inverters, a novel diagnosis method based on generative adversarial network (GAN) and convolutional neural network(CNN) is proposed. Firstly, the phase current is used as the fault-sensitive signal, and the fast Fourier transform(FFT) is performed to obtain the frequency domain features, and the normalization preprocessing is performed. Then, the GAN model is used for confrontation training to generate virtual samples by few real sample characteristics, in order to get balanced samples with different fault modes. Finally, convolutional neural network model is built to complete the power inverter fault diagnosis. The experimental results show that GAN-CNN can effectively improve the diagnosis accuracy and stability in the case of sample imbalance.
  • 若对本资源有异议或需修改,请通过“提交意见”功能联系我们,平台将及时处理!
来源
关键词
相关推荐
可试看前3页,请 登录 后进行更多操作
试看已结束,会员免费看完整版,请 登录会员账户 或申请成为中国电源学会会员.
关闭
温馨提示
确认退出登录吗?
温馨提示
温馨提示
温馨提示
确定点赞该资源吗?
温馨提示
确定取消该资源点赞吗?
温馨提示
确定收藏该资源吗?
温馨提示
确定取消该资源收藏吗?
温馨提示
确定加入购物车吗?
温馨提示
确定加入购物车吗?
温馨提示
确定移出购物车吗?