Abstract:

In view of the problem that the potential defects of generator stator and rotor seriously affect the safety and stability of unit operation, an intelligent diagnosis algorithm of generator stator and rotor based on VGG network is proposed. Compared with Alex network, VGG network uses multiple stacked small size convolution filters instead of large size convolution filters, which reduces the size of algorithm parameters and deepens the depth of network structure. The proposed algorithm includes two parts: offline training and online monitoring. The former part uses the local server generator stator and rotor historical images for learning and training to obtain the VGG network model which meets the accuracy requirements. The latter part uses the trained VGG network model to realize the online real-time monitoring of generator stator and rotor. The simulation results show that compared with the Alex network, the proposed algorithm has faster convergence speed, smaller calculation error and higher recognition accuracy for generator stator and rotor defects.

Key words: stator and rotor, convolutional neural network, diagnosis, defect, Alex network, generator, defect identification, online real-time monitoring