Abstract
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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: CPSS Transactions on Power Electronics and Applications
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
